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An analytical plate bending model is used in a novel measurement method that is being developed for feedback control of material properties in paper manufacturing. The model involves an efficient material model for paper yet allowing a selected level of material complexity. In this study, the effects of the simplifications are studied by comparing the results with a discretized model applied in finite element methods. The boundary conditions required for specifying the censoring location in the real system proves to be challenging and a specific combined load distribution might be necessary to apply the developed model.

Scientific research projects play a positive role in the development of scientific researches. Although researchers continue to study the paper output of projects, the correlation between projects and papers in research contents and topics is still unclear. Based on the data set NSFCD from China, the topics of papers are divided and the matching degree between projects and paper topics is defined to reveal the relationship between projects and papers. The results show that the research contents of projects and papers have some relevance, but the low values of matching degree indicate that the correlation between them is not evident. This work proposes a quantitative method for evaluating projects with paper topics and suggests that the quality of projects’ outputs needs to be strengthened. The papers published by project participants should be related to project contents.

This paper presents a techno-economic analysis of an absorption-based CO2capture process using Monoethanolamine (MEA) as the solvent for pulp and paper mill limekiln. The flue gas specifications were obtained from published limekiln data of a theoretical pulp and paper mill. The process was simulated in Aspen Plus and linked to CAPCOST using a python script for the cost calculations. The CO2capture cost estimates were compared to the only CO2capture costs data available in the literature for limekiln flue gas. Comparing the cost breakdown between the published data and this study, the capital cost difference was found to be highest for the stripper and the compression and dehydration sections. Further, the capture cost sensitivity analysis, evaluating the impacts of key parameters, including flue gas CO2mol%, MEA, electricity, and steam, showed that the capture costs varied from $70 to $82 per tonne of CO2captured.

Jan Willems introduced the system-theoretic notion of dissipativity in his foundational two-part paper which appeared in the Archive of Rational Mechanics and Analysis in 1972. Even earlier, in a likewise pivotal 1971 IEEE Transactions on Automatic Control paper, he investigated infinite-horizon least-squares optimal control and the algebraic Riccati equation from a dissipativity point of view. This note revisits infinite-horizon optimal control leveraging strict dissipativity. We discuss the interplay between dissipativity and stability properties in continuous-time infinite-horizon problems without assuming linear dynamics or quadratic cost functions. Finally, we compare our recent results from Faulwasser and Kellett (2021) to the original findings of Willems (1971).

Image enhancement is the most fundamental steps used to manipulate an image so that the result is more acceptable than the original image, with a try for preserving every detail, in the field of digital image processing. With the high demand of computer visual technology, digital image processing concepts have been rapidly used in many real world applications for information gathering, such as: industrial productions, medical images, video monitoring, intelligent transportations, etc. However, image captured under low light has some issues such that it may contains noise, undergoes color distortion and also may undergoes with some information loss. Again industry like medical and satellite we can't afford single information to be miss, so for that purpose low light image enhancement is a must needed concept for current emerging world. Here in this paper, we analyze different methods of image enhancement and their result, mainly this paper focus on different fusion-based methods, other methods such as retinex based methods, fuzzification etc.

Today, we have many options for hardware and software technology solutions for the migration of the manufacturing sector to an industry 4.0. However, there are many cases in which the adoption of new technologies has been slowed mainly due to uneven growth in the industry, little standardization, poor systems’ integration, lack of knowledge, and the lack of providers who understand the real needs of companies. The purpose of this work is to present a methodology for selecting the proper technology that was developed after characterizing and quantifying the industry's requirements. Our methodology presents criteria for evaluating and selecting technology providers that focus on four dimensions: technical criteria, purchase criteria, additional services for the operation of the products, and operating costs. This methodology represents a practical way to qualify technology providers, assigning them a quantitative score so that the decision-making process is much faster, more convincing, and compelling, especially when adopting new technology. The methodology was built and used into a paper mill for selecting sensor suppliers to implement a condition monitoring system that captures vibration signals at different points of paper presses. The evaluation was successful, resulting in a winning provider being selected and the IoT sensor-based system implemented.

Aiming at the problems with low efficiency and low standardization in the establishment of complex product Research & Development(R&D) process, a combination and optimization method for R&D activities is proposed in this paper. Firstly, process module is established by combining related R&D activities using process network diagram and description text. Then, a method is established for similarity representation between process module and design requirements, on which the module reorganization is based. And optimal combination scheme of modules is obtained by an adaptive genetic algorithm. Finally, the effectiveness of proposed method is verified through the example of a product R&D process.

This paper provides a conceptual analysis of the role of business continuity management (BCM) as a key enabler of supply chain resilience (SCRE). Through an analysis of the extant literature, the authors show how BCM can facilitate and enhance key constructs that are directly linked to SCRE. The results show that the existing BCM lifecycle – already widely used in the industry – has the potential to play a decisive role in boosting agility, flexibility, velocity, visibility, and collaboration, as well as response and recovery capabilities.

Nowadays, technologies and strategies to bring processes toward the Industry 4.0 paradigm go beyond simple paper proposals and focus on their implementation. This article presents a case study of University-Business collaboration focused on technology transfer as part of a viable solution to real problems in the company. The objective of the collaborative project was the transfer of knowledge and use of technologies by the research group and, together with the knowledge based on the experience of the company's maintenance department, to solve a real problem in the industry. Therefore, we set out with the industry the objective of implementing a system for early detection of failures in press bearings to ensure that maintenance personnel performs maintenance actions on time. We combined agile methodologies and design thinking for the implementation because agile methodologies allow controlling the development progressively, and design thinking focuses on the user's needs. The implementation process was executed in six phases: empathize, ideate, define, test, prototype, and implement. The project was successfully implemented in six months and met industry requirements. Combining agile methodologies and design thinking was fundamental for the implementation, as it was possible to get feedback from industry personnel and to have materialized progress in each meeting.

Machining distortion caused by residual stresses is one of the major challenges in the production of thin-walled monolithic parts, which are widely used in the aerospace industry. The relevant influencing factors include the selected process parameters, the machining sequence as well as the used fixture system. This paper presents an adaptive fixture system for reducing machining distortion caused by residual stresses in milling. To validate the advantage of this system, machining distortion experiments are conducted on monolithic parts made of Ti-6A1-4V using a conventional and the adaptive fixture system.

Intrinsically, many real control problems are mathematically challenging. Thus, we often relax the original problem to obtain a tractable problem. In this paper, we consider a computational technique to solving a nonlinear stochastic optimal control problem in a direct manner. Our computational approach employs the idea of the recently developed technique, called deep unfolding. Deep unfolding is a deep learning method that is model-based, used to accelerate iterative algorithms. For the finite-horizon optimal control problem, we regard each state transition of the discrete-time dynamical system with control inputs and disturbances as an iteration step. Then, we unfold those state transitions into the layers to construct a deep neural network so that each of those layers contains a trainable parameter of the control input. This produces a computational graph. Once the computational graph is fixed, the control inputs are computed by training the deep neural network. This open-loop method is then integrated into model predictive control to close the loop. The advantages of our computational technique are not only the ability of handling nonlinear state transitions and non-Gaussian disturbances, but also its simplicity. The feasibility and benefit of the proposed technique are demonstrated by numerical experiments using a continuous stirred tank reactor model, which is highly nonlinear.

In this paper, adaptive radial basis function neural network based sliding mode control of a fast acting superconducting magnetic energy storage (SMES) is reported. With the converter interface SMES is installed and connected with the wind-diesel micro grid to carry the required power exchange to improve the system frequency. With sliding surface design and neural network using a radial basis function, a sliding mode controller action is used to control the converter, and achieve the desired operation of SMES. Computer simulations are performed and presented to show the superiority of the proposed methodology with the system subjected to load and wind power variations.

Stabilization problem of inverted pendulum system is one of the challenging issues to the researchers. To stabilize this unstable inverted pendulum system, linear quadratic regulator (LQR) controller has been designed along with the Kalman filter. Kalman filter estimates the state variables of the system and LQR controller takes these estimated state variables as inputs. The performance of this proposed controller has been tested for both stabilization and tracking problem of inverted pendulum system. Both the stabilization and tracking responses of the closed-loop system satisfies the required performance specifications. The responses of the closed loop system with Kalman filter and without Kalman filter also have been compared. Kalman filter based closed-loop system eliminates the effects of process and sensor noises that are present in the outputs of the system. The performance of this proposed controller is also compared with LMI based robust controller that had been earlier designed for the same system.

The paper describes various brachistochrone problem statements. In the beginning an approach to solving the brachistochrone problem with a nonzero initial velocity using optimal control methods is presented. The substantiation of the transition to zero initial velocity is also provided. The second type of problem statement takes into account explicit restrictions on the curvature of the trajectory. The solution is obtained by using the Pontryagin’s Maximum Principle. The paper demonstrates that the optimal solution includes both regular and singular trajectory parts.

The paper presents an identification strategy to obtain relations between time delays of a transport model for an evaporating liquid film and corresponding flow/liquid properties. To this end, step-response based pilot plant experiments referring to the falling film evaporator process are conducted. In this context, the time-delay relations are identified offline by minimizing the output error between the model and experiment. In subsequent validation experiments, we observe that modeled and measured outputs are in good accordance. Within this framework, we conclude that the influence of evaporation on the liquid’s time-delay behavior can be neglected.

Community detection plays an important role in understanding and exploiting the structure of complex systems. Many algorithms have been developed for community detection using modularity maximization or other techniques. In this paper, we formulate the community detection problem as a constrained nonsmooth optimization problem on the compact Stiefel manifold. A Riemannian projected proximal gradient method is proposed and used to solve the problem. Numerical experimental results on synthetic benchmarks and real-world networks show that our algorithm is effective and outperforms several state-of-art algorithms.

In this work we present a novel classification model that can detect kick-scooters from inertial and pressure sensors. The detection is performed with kick-scooters being trained with other activities and transportation modes including still, walking, biking, taking bus and tramway. Results show that kick-scooters can be precisely detected up to 99% for three different sensor placements: on-foot, waist-attached and in the trouser’s pocket. Thus, this paper provides a first contribution where kick-scooters can be classified and studied for further applications such as mobility behavior analysis and navigation.

Intelligent manufacturing and automated systems several complex control tasks must be carried out. A possible way for improving the performance level of the systems is the application of machine-learning-based agents, e.g. neural networks in the control loop. A novel challenge of these complex systems is to provide analysis and synthesis methods, with which their performance levels can be assessed. The paper proposes analysis method for tracking control systems, whose control loop contains feed-forward neural networks. Through the method the asymptotic stability and the tracking performance through decay rate are assessed. The proposed method is based on the linear approximation of the closed-loop system, and thus, a polytopic set of linear systems is resulted. Using the resulted polytopic system an analysis method based on an optimization is formed, whose result approximates the decay rate of the system. The effectiveness of the method is illustrated through a benchmark example, i.e. the torque control of a one-degree-of-freedom robotic arm.

Modern engineering systems include many components of different types and functions. Verifying that these systems satisfy given specifications can be an arduous task, as most formal verification methods are limited to systems of moderate size. Recently, contract theory has been proposed as a modular framework for defining specifications. In this paper, we present a contract theory for discrete-time dynamical control systems relying on assume/guarantee contracts, which prescribe assumptions on the input of the system and guarantees on the output. We then focus on contracts defined by linear constraints, and develop efficient computational tools for verification of satisfaction and refinement based on linear programming. We exemplify these tools in a simulation example, proving a certain safety specification for a two-vehicle autonomous driving setting.

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results.

In this paper the problem of synchronisation of multiple robotic manipulators using Hoo robust distributed control systems with respect to the parameter uncertainties and disturbance inputs acting on the manipulators is addressed. Robust synchronising controllers only use the information of outputs of the manipulators, and the corresponding parameters of these output-feedback controllers are designed by computing a series of linear matrix inequalities instead of solving the complex differential (e.g. Hamilton-Jacobi) (in)equalities. The proposed controller can guarantee H∞robust performance with respect to the external disturbance inputs and parameters uncertainties, asymptotic stability and synchronisation in the networked manipulators. Using an illustrative example we compare the results extracted in this paper to other works existing in the literature.

This paper improves and evaluates in detail a previous work of the author dealing with position, size and orientation estimation of fixed ground obstacles in aircraft sense and avoid. The improvement is the better conditioning of the system of equations by the shift of one known variable. Detailed evaluation means Monte-Carlo simulation and comparison of the disc and line-based parameter estimation methods considering also non-straight own aircraft trajectories. This is the focal point of the article as only straight trajectory results were compared until now. The mean estimation errors (in percentages) and their standard deviations with the two different methods are compared. Finally, the line-based method was better applicable for rectangular objects as expected.

The paper presents a small-scale electric vehicle framework for vehicle control education and research. The main goal of the project is to serve as a good experimental platform for the students on any level of vehicle mechatronics education. It offers wide range of possibilities for embedded system, control design and machine learning applications. The proposed system is a redesigned version of our former experimental vehicle framework. The project was scheduled for two main goals. One was to design and implement a Robot Operating System based vehicle with Ackermann steering. The other was to develop a platform, which can be highly integrated into vehicle control education. In the paper the system architecture, the sensors and the control units are detailed, furthermore the educational benefits and some use cases are presented.

We present the application and experimental validation of discrete-time control implementation based on symplectic integration - in short referred to as symplectic discrete-time control - to a serial robot manipulator with flexible-joints. Applying the implicit midpoint rule as a simple and popular symplectic integration scheme to both the model of the sampled system and the desired target dynamics, yields a discrete-time control law with the same structure as in continuous time. The arguments are, however, predicted stage values on the subsequent sampling interval resulting from a half implicit Euler step for the target system. The approach can be easily extended to a purely position-based feedback law. The experimental validation on a lightweight robot (KUKA LWR IV+) confirms the expected improvements at low sampling rates, with an increase in the assignable closed-loop stiffness of up to 29 %.

Assembly systems are constantly upgrading towards Assembly 4.0 through penetration of emerging technologies in Industry 4.0. In this context, the human-cyber-physical system has been proposed recently and engineered to improve human abilities and capabilities to interact with machines in cyber-physical spaces dynamically. However, most existing frameworks are still not adaptive enough to integrate human in the loop due to the lack of full consideration of human limitations. This research proposes a human-cyber-physical workstation system (HCPWS) for assembly resources digitisation, interconnection and interaction through embedded CPS technologies. Computer vision is deployed for real-time dynamic information analysis within the workstation, and a prototype is built for HCPWS implementation and verification.

In production, poor ergonomic environments not only lead to increased workloads and health hazards for employees but also tend to reduce efficiency and quality. Recently, the human-cyber-physical (HCP) system has been proposed and widely studies to meet human capabilities and limitations. However, most existing frameworks are still not adaptive enough to integrate humans into a smart production environment due to a lack of real-time individual human factors digitalization. This research proposes an HCP workstation model for comprehensive assembly resources digitalization and autonomous interaction by CPS enabling technologies. Based on this, an adaptive control system has been developed for proactive ergonomic risk mitigation. Computer vision is deployed for real-time individual ergonomic evaluation and a prototype has been set up for functional validation.

Increasing lightweight constructions ensures a growing interest in economically produced magnesium sheets. The process of magnesium twin roll casting makes this possible by combining several different process steps into one process. However, due to the harsh process conditions, it is not possible to directly record the relevant process parameters to control product properties. Therefore, in this article will be discussed, how twin roll casting process can be transformed into a property-controlled process with a soft sensor. The digital image of the process is created by usage of an extended layer model, developed as a digital twin for offline computation and whose calculation times are many times shorter than in the FEM method. This makes it possible to train the digital twin and use it for online controlling in the TRC process. As a result, from the sensor data on the roller surface, which in turn forms the computational core of the required soft sensor, conclusions can be drawn about the state of the material in the rolling gap.

The adoption of digital manufacturing in small to medium enterprises (SMEs) in the manufacturing sector in the UK is low, yet these technologies offer significant promise to boost productivity. Two major causes of this lack of uptake is the high upfront cost of digital technologies, and the skill gap preventing understanding and implementation. This paper describes the development of software wrappers to facilitate the simple and robust use of a range of sensors and data sources. These form part of a common architecture for data acquisition in the Digital Manufacturing on a Shoestring project. We explain the existing Shoestring demonstrator architecture, and discuss how a ‘crash-only’ microservices architecture would improve fault tolerance and adaptability of the system.

This paper focuses on the detection of cyber-attacks in a timed probabilistic setting. The plant and the possible attacks are described in terms of a labeled continuous time Markov model that includes both observable and unobservable events, and where each attack corresponds to a particular subset of states. Consequently, attack detection is reformulated as a state estimation problem. A verification methodology is described using a parallel-like composition of the Markov model and its logical observer. The construction of this parallel composition allows us to (i) concisely characterize the set of attacks that can be detected based on the sequences of observations they generate, and (ii) compute performance indicators of interest, such as the a priori probability of an undetectable attack, the average detectability, and the mean delay to detection.

This paper presents a real-time optimal motion planner algorithm for road vehicles. The method is based on a cubic spline trajectory planner which is able to plan a set of vehicle motions driving from a given initial state to a required final state. Maximal dynamical feasibility and passenger comfort are ensured by minimizing the lateral acceleration and tracking errors as the vehicle moves along the trajectory. Tracking of the planned motion is realized during planning and execution as well by separate longitudinal and lateral controllers. efficient implementation and small number of optimization variables enables real-time usage. The trajectory planner is first tested in a quasi real-time simulation environment and then under real working conditions at the dynamic platform of proving ground ZalaZone with a completely drive-by-wire Smart Fortwo. Measurement results are presented and analyzed in detail, and possible future research directions are mentioned.

Wave energy has a significant part to play in providing a carbon-free solution to the world’s increasing appetite for energy. In many countries, there is sufficient wave energy to cater for the entire national demand, and wave energy also has some attractive features in being relatively uncorrelated with wind, solar and tidal energy, easing the renewable energy dispatch problem. However, wave energy has not yet reached commercial viability, despite the first device designs being proposed in 1898. Control technology can play a major part in the drive for economic viability of wave energy and this paper charts the progress made since the first wave energy control systems were suggested in the 1970s, and examines current outstanding challenges for the control community.

In this paper, a class of nonlinear systems is considered, where the nominal system representation is allowed to be non-minimum phase. A sliding surface is proposed which is a function of the measured system output and an estimated state. A linear coordinate transformation is introduced so that the stability analysis of the reduced order sliding mode dynamics can be conveniently performed. A robust output feedback sliding mode control (OFSMC) is then designed to drive the system states to the sliding surface in finite time and maintain a sliding motion thereafter. A simulation example is used to demonstrate the effectiveness of the proposed method and the method is successfully applied to an inverted pendulum.

A flexible architecture is developed with the purpose of supporting education and research on the field of autonomous vehicles. A pure electric vehicle is equipped with on-board computational units, sensors and actuator interfaces. This paper presents the current status of the control system and its validation by means of navigation experiments. With the cascade control architecture, problems of actuator dead-zone, sensor offset errors, path tracking and redesign for obstacle avoidance are addressed.

Battery electric buses take an increasingly larger market share and attract much attention from bus fleet operators to undercut urban emissions limits. But meanwhile it also becomes a challenge to operators to determine the required battery capacity to be sufficient for the specific transport operations. The deployment planning includes to select appropriate bus model specifications, battery characteristics, charging parameters, timetable schedules and further dependencies under the aspect of ownership costs. Regarding battery characteristics however, most electrochemical battery models focus more on the internal structure, which have a worse compatibility on a system level. This paper aims to build up a lithium-battery model based on the third-Thevenin equivalent circuit, considering both complexity and accuracy. Firstly, this battery model is implemented with lookup tables to represent the values of each circuit component, which results in a mean error of 1.98 mV or 0.06%, compared with the original measurement data. Then a general control model of electric vehicles is introduced to cooperate with this battery model in a system, combined by the current flowing between these two models. Finally a simulation is proceeded, employing the real data from Solaris® Urbino 12 Electric Bus, which provides a reliable SOC estimation for the full day operation.

In this paper, a distributed diffusion unscented Kalman fltering algorithm based on covariance intersection strategy (DDUKF-CI) is proposed for object tracking. By virtue of the pseudo measurement matrix, the standard unscented Kalman fltering (UKF) is transformed to the information form that can be fused by the diffusion strategy. Then, intermediate information from neighbors are fused based on the diffusion framework to attain better estimation performance. Considering the unknown correlations in sensor networks, covariance intersection (CI) strategy is combined with the diffusion algorithm. Moreover, it is proved that the estimation error of the proposed DDUKF-CI is exponentially bounded in mean square using the stochastic stability theory. Finally, the performances of the proposed algorithm and the weighted average consensus unscented Kalman fltering (CUKF) are compared in a target tracking problem with a sensor network.

Based on the nested-saturation technique, this paper investigates the global stabilization problem for a class of upper-triangular nonlinear systems with uncertain measurement functions. By imposing certain assumptions on the uncertain powers, a state-feedback controller, only involving the known parameters, is designed to locally stabilize the nonlinear system. Then, a saturated controller is constructed by combining the nested function and the local stabilizer. With appropriate saturation level, it can be proved that the saturated controller is able to make the closed-loop system globally asymptotically stable. Finally, a simulation example is presented to demonstrate the effectiveness and robustness of the proposed control scheme.

In this paper, we propose a hierarchical control strategy for a line of electric buses with the double objective of minimizing energy consumption and providing regular service to the passengers. The state-space model for the buses is formulated in space rather than in time, which alleviates the need for integer decision variables to capture their behavior at bus stops. This enables us to first assemble a fully-centralized multi-objective line problem in the continuous nonlinear optimization framework. It is then reassembled into a hierarchical structure with two levels of control in order to improve on scalability and reliability. This new supervisory structure consists of a centralized line level controller which handles the time headway regularity of the buses, and of decentralized bus level controllers which simultaneously manage the energy consumption of each individual bus. Our method demonstrates good battery energy savings and regularity performances when compared to a classical holding strategy.

In this paper, an event-triggered, data-rate constrained observer for discrete-time linear systems with perturbations is presented. The system is connected to a remote location by a communication channel which can only transmit limited numbers of bits per time interval. The system is subject to perturbations in its state as well as errors in the output measurement. The objective is to reconstruct estimates of the state at the remote location, by sending messages over the communication channel. A new type of data-rate constrained observer which can be more efficient in terms of communication rate is presented. Relation between an admissible communication rate and the system parameters is evaluated. The observer’s efficiency is illustrated by simulations.

Environment perception and situation awareness are keystones for autonomous road vehicles. The problem of maneuver classification for road vehicles in the context of multi-model state estimation under model uncertainty is addressed in this paper. The conventional approach is to define different motion models that match the desired type of movements. In this work we used a single motion model as a starting point and applied constraints to construct such filters that are fine tuned for the predefined maneuvers. The estimation is carried out in the interacting multiple model framework, where the elemental filters are constrained Kalman filters. To capture the characteristics of the considered maneuvers linear equality and non-equality state constraints were used. The performance of the proposed method is demonstrated in a simulation environment participating an observer and a maneuvering vehicle.

In this paper, a novel decentralised robust state feedback sliding mode control is presented to stabilise a class of nonlinear interconnected systems with matched uncertainty and unknown interconnections. A composite sliding surface is designed, and a set of conditions are developed to guarantee that the corresponding sliding motion is uniformly asymptotically stable. Then, a decentralised state feedback sliding mode control is proposed to drive interconnected systems to the designed sliding surface in finite time, and sliding motion occurs thereafter. The bounds on the uncertainties and interconnections are known nonlinear functions, which are employed in the control design to reject the effects of uncertainties and unknown interconnections to enhance the robustness. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed control strategy.

In this paper, least-squares support vector machine (LS-SVM), whose parameters are updated by unscented Kalman filter (UKF), is adopted in the generalized predictive control (GPC) of a system with general multicompartment lung mechanics. Gaussian kernel function is employed since it presents a good approximation to the inner product of nonlinear mapping possessed in the SVM formulation. In the SVM literature, it is well known that the width parameter σ of the Gaussian kernel function has an important effect on the performance. However, it is not possible to train that parameter together with the other parameters of SVM when using linear least squares. This is why we use UKF for parameter adaptation in the SVM formulation. At each time instant of the control task, all parameters of the LS-SVM model, including σ, are tuned simultaneously. Another reason to employ UKF is; it avoids the suboptimal solutions caused by linearization based filters, e.g., extended Kalman filter. Due to these facts, we train the SVM model using UKF and it will be referred to as the UKF-SVM model. Simulation results concerning the application of UKF-SVM based GPC to a multicompartment lung mechanics model yields plausible performance using small amount of support vectors even when there are time-varying lung parameters and disturbance of high level affecting the system. The adopted approach can also be useful when there is not any knowledge of the system dynamics, i.e., black box. Note that, multicompartment lung mechanics system is a stand-in model that can mimic the behavior of human lung. Thus, it is appropriate for hardware-in-the-loop simulation which opens a path to the real-patient-tests of mechanical respiratory systems in the future.

This paper proposes Runge-Kutta neural disturbance observer to enhance the robustness of PID control of a system with general multicompartment lung mechanics. It is designed to observe the states of a particular type continous time, single-input single-output system where the states cannot be measured but can be observed through the single output and there exists parametric uncertainity or disturbance affecting the underlying system. It utilizes artificial neural network to estimate the disturbance online. Once an accurate disturbance estimation is obtained, it is incorporated in the system state equation and passed through the well-known Runge-Kutta integrator to predict the state values. Hence, the predicted states are obtained considering the disturbance and more robust state observation is achieved. The proposed observer is simple and easy to implement. Adaptation of the neural network is performed using gradient descent with an adaptive learning rate which guarantees convergence. The simulation results demonstrate that the proposed observer gains a significant success in enhancing the robustness of PID control at even high level of disturbance. Note that, multicompartment lung mechanics system is a stand-in model that can mimic the behavior of human lung. Thus, it is appropriate for hardware-in-the-loop simulation which opens a path to the real-patient-tests of mechanical respiratory systems in the future.

Observabililty is an important topic of Boolean control networks (BCNs). In this paper, we propose a new type of observability named online observability to present the sufficient and necessary condition of determining the initial states of BCNs, when their initial states cannot be reset. And we design an algorithm to decide whether a BCN has the online observability. Moreover, we prove that a BCN is identifiable iff it satisfies controllability and the online observability, which reveals the essence of identification problem of BCNs.

Many control tasks can be formulated as tracking problems of a known or unknown reference signal. examples are motion compensation in collaborative robotics, the synchronisation of oscillations for power systems or the reference tracking of recipes in chemical process operation. Both the tracking performance and the stability of the closed-loop system depend strongly on two factors: Firstly, they depend on whether the future reference signal required for tracking is known, and secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data while guaranteeing trackability of the modified desired reference predictions within the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimisation. Two specific scenarios, i.e. asymptotically constant and periodic REFERENCES, are discussed.

Traditional extrusion based additive manufacturing (AM) is realized using a 3 degrees of freedom (DOF), translation only, 3D printer. It then follows that the printer must be larger than the printed part. One way of enabling AM on a larger scale is to combine AM with robotics. By using a 6 DOF robot manipulator to extrude a fast-curing material, the workspace of the build would be greatly expanded. In addition, since the structures would no longer have to be built with the bottom-up or top-down approach which is necessary for most existing forms of AM, the flexibility of the building process would also increase. This could possibly reduce the need for support structures to the point of only relying of anchoring and stabilizing. In this paper, a method for generating a path for AM using robot manipulators that takes advantage of the robot’s DOF is presented. The path is generated based on simple surfaces in CAD models. First, the surface is sampled and the samples are gathered in a point cloud. Then, a path is generated based on the point cloud. Three different approaches for generating a path are tested where the weighted greedy choice algorithm gave the most promising result. With this algorithm, printing along curved surfaces and in nonlinear paths are enabled.

Reverse Osmosis (RO) is an evolving membrane-based technology for water desalination that started to gain increased popularity in the light of the increased global water demand due its high efficiency and low carbon footprint. This paper presents a full-scale Seawater Reverse Osmosis (SWRO) desalination plant simulator using MATLAB/Simulink, which is a user-friendly and commonly used simulation software. The simulator has been validated using the operational data from a local plant and it allows simulating the system behavior under different operating conditions with high flexibility and minimal cost. It can be used to analyze the plant performance under different operating conditions, and for research for health monitoring applications and in the cybersecuity area.

A two-on-one pursuit-evasion differential game is considered. The setup is akin to Isaacs’ Two Cutters and Fugitive Ship differential game. In this paper it is however assumed that the three players have equal speeds and the two cutters/pursuers have a non-zero capture radius. The case where just one of the Pursuers is endowed with a circular capture set is also considered. The state space region where capture is guaranteed is delineated, thus providing the solution of the Game of Kind, and the players’ optimal state feedback strategies and the attendant value function are synthesized, thus providing the solution of the Game of Degree.

To estimate and compensate disturbances effectively, disturbance observer (DOB) has been widely employed in industrial field. This paper is dedicated to designing DOB by directly utilizing frequency response data. By transforming all the non-convex constraints into convex form, the bandwidth of DOB is maximized through iterative convex optimization process. Simulation results have verified the effectiveness of the proposed method.

In this paper, consensus for a network of dynamical systems which communicate over data-rate constrained communication channels is considered. Each system in the network is equipped with a sensor and an actuator which are at locations remote from one another. In order to transmit the state of any system to any of the actuators, the sensors use data-rate constrained communication channels. The actuators then use the messages to determine control inputs such that the systems achieve a particular type of consensu. Sensor/actuator pairs that achieve that particular type of consensus are called consensus protocols. In this contribution, an efficient in terms of required data-rates consensus protocol is presented. For the protocol, a theorem proving conditions on the sufficient minimal data-rates to implement it is provided. The sufficient data-rate is proven to depend on the singular values of the linear part of the mapping of the systems in the network. Finally, an example is provided in the form of consensus for a network of harmonically forced bouncing ball systems, for which an analytical bound is provided on the sufficient outgoing channel rates.

Aggregated dynamic equivalent models of active distribution networks (ADNs) are commonly derived using the measurement-based approach. This method exploits acquired data in order to estimate the model parameters using system identification techniques. However, most of the approaches assume that the system maintains the same dynamics for different operating conditions, even though the load mix and the distributed generation (DG) composition are constantly changing. To this end, this paper presents a novel method, which can be used as the first step of the system identification procedure, in order to take into account different system dynamics in ADN modeling. To do so, three unsupervised learning methods for clustering the various dynamic behaviors are introduced, yielding groups of measurements that represent different dynamics. In this context, the proposed methods leverage four clustering algorithms of different notion and complexity, namely k-means++, k-medoids, fuzzy c-means (FCM) and hierarchical clustering. To assess the validity of the proposed approach, real measurements acquired within a year in six real substations in Southern Germany are processed. The results highlight the remarkable difference in system dynamics justifying the necessity of an initial cluster analysis. Finally, the ratio of ”Within Cluster sum of squares” to ”Between Cluster Variation” (WCBCR) is deployed to compare the effectiveness of the clustering algorithms.

This paper presents a model reference adaptive controller for linear time invariant systems with unknown parameters. The adaptive laws used to estimate the controller parameters are based on fractional differential equations, whose orders are switched among a fractional value in the interval (0,1) and 1 at certain time instants. Boundedness of the signals in the resulting controlled system is proved using recent results. A simulation study is provided for a second order system to show how the proposed control strategy can improve the behavior and decrease the control energy used, compared to the classic model reference adaptive controllers and fractional order model reference adaptive controllers with non-switched adaptive laws.

This paper is concerned with event-triggered robust attitude control of networked spacecraft with actuator saturation and persistent bounded disturbance via Takagi-Sugeno (T-S) fuzzy approach. Based on aperiodic sampling, a discrete-time event-triggered scheme avoiding Zeno phenomenon naturally is adopted to reduce communication burden within network. Based on the T-S fuzzy model of spacecraft, an event-triggered fuzzy controller subject to saturation constraint is established to perform robust attitude stabilization of spacecraft. Furthermore, in comparison with the most often used H∞performance, L∞-gain performance is employed to handle the persistent bounded disturbances. By virtue of convex optimization method, the problem of controller synthesis is formulated in terms of a set of linear matrix inequalities (LMIs). Finally, simulation results are provided to prove the effectiveness of the fuzzy control scheme.

A self-tuning proportional-integral control law prescribing motor torques was tested in experiment on a three degree-of-freedom wave energy converter. The control objective was to maximize electrical power. The control law relied upon an identified model of device intrinsic impedance to generate a frequency-domain estimate of the wave-induced excitation force and measurements of device velocities. The control law was tested in irregular sea-states that evolved over hours (a rapid, but realistic time-scale) and that changed instantly (an unrealistic scenario to evaluate controller response). For both cases, the controller converges to gains that closely approximate the post-calculated optimal gains for all degrees of freedom. Convergence to near-optimal gains occurred reliably over a sufficiently short time for realistic sea states. In addition, electrical power was found to be relatively insensitive to gain tuning over a broad range of gains, implying that an imperfectly tuned controller does not result in a large penalty to electrical power capture. An extension of this control law that allows for adaptation to a changing device impedance model over time is proposed for long-term deployments, as well as an approach to explicitly handle constraints within this architecture.

In this contribution the control of a grid-side inverter of a wind energy system (WES) is addressed. Unlike the well-known VOC control strategy, the proposed controller is based on a system model that does not use Park’s transformation, i.e. the model is not formulated in d/q-coordinates. Therefore, the system model contains non-linearities, but the validity of the model does not rely on an accurate phase angle detection for the transformation and not on the assumption of a balanced three-phase system. The non-linearities are investigated with the stability theory of Lyapunov and the remaining linear parts can be addressed with methods form linear control theory. The presented control strategy is developed in a simulation environment and numerical simulations were performed. Results are presented that show the effectiveness of the method.

In this article a fault detection algorithm for aircraft position measurements i proposed using redundant sensor information during landing scenarios. This work was developed in the framework of the VISION EU H2020 research project. The aircraft’s position can be determined via instrumental landing system, GPS and camera measurements. Considering these three sources a two out of three voting logic can be developed. After transforming the measured data sets to a common format two different methods are constructed to execute voting. The first is simple and well known thresholding where the measured position values are compared pairwise and thresholding violations registered. As dissimilar data noise strengths can make thresholdin unreliable the second method proposed by the authors is supplemented with an additional statistical evaluation where the measurements undergo a two-sample Z-test. Both methods were evaluated off-line with Monte-Carlo computer simulation. The tests showed that the proposed statistical method outperforms the straightforward thresholding approach.

This article is the improvement of the author’s previous work which considered position and size estimation of steady ground obstacles applying disc projection model and a constant velocity straight trajectory assumption. In case of square objects (trucks, buildings) the disc projection model can be a very rough approximation and so its improvement is required. This paper presents a method to estimate the orientation of the square object and applies a line projection model which gives more precise size and position estimates. The formulas of the previous work were also improved to consider non-constant velocity non-straight trajectories but tested only with constant velocity and straight trajectory assumptions to first prove applicability in a simpler case and because of the limited length of the paper.

The paper presents a mixed reality-based approach for interactive control of robotic manipulators and mobile platforms. In particular, we designed an interactive and understandable interface for human-robot interaction. The interface provides tools for robot path programming and visualizes it. Path visualization helps workers understand robot behavior, it is important for safety human-robot interaction. The paper presents an architecture of that system and the implementation for an industrial robot KUKA iiwa and mobile robot platform Plato. The main issue of a multi-platform system is related to the synchronization of coordinate frames for all elements. To deal with this problem we realized 3 setting options: manually, by a camera with markers, point clouds processing. We implemented our interface on Microsoft HoloLens and evaluated it on users.

This paper presents our initiative on the design and building of a convertible aircraft for fault tolerant control. The main advantage of convertible over conventional aircraft is that they can take-off and land vertically, practically eliminating the need for a long runway. At the same time, fixed wings allow to improve energy efficiency with respect to multi-copters. To satisfy these objectives, we have chosen a bi-rotor delta-wing design which is described in the first part of the paper. The paper gives a description of the main educational activities of this project emphasizing its multidisciplinary nature. Indeed, one important aspect of this project is that we have had the chance to work with students from various specialities (such as mechanical engineering, embedded systems, avionics and automatic control) on bachelor and master’s levels. Moreover, the end result of this project should provide us with a very interesting experimental test-bench for teaching and research purposes. Finally, this activity is also the opportunity for our students to develop their soft skills such as project management and communication.

We present a novel solution to the attitude control problem of ground vehicles by means of the Active Front Steering (AFS) system. The classical feedback linearization method is often used to track a reference yaw dynamics while guaranteeing vehicle stability and handling performance, but it is difficult to apply because it relies on the exact knowledge of the nonlinearities of the vehicle, in particular the tire model. In this work, the unknown nonlinearities are real-time learnt on the basis of the universal approximation property, widely used in the area of neural networks. With this approximation method, the Uniform Ultimate Boundedness (UUB) property with respect to tracking and estimation errors can be formally proven. Preliminary simulation results show good tracking capabilities when model and parameters are affected by uncertainties, also in presence of actuator saturation.

We consider non-intrusive load monitoring by a sophisticated adversary that knows the load profiles of the appliances and wants to determine their start-finish times based on smart-meter readings. We prove that the expected estimation error of non-intrusive load monitoring algorithms is lower bounded by the trace of the inverse of the cross-correlation matrix between the derivatives of the load profiles of the appliances. This is an interesting observation illustrating that the derivatives of the load profiles are more important than the profiles themselves for non-intrusive load monitoring (i.e., small rapidly-changing loads are easier to identify than large, yet slowly-varying ones). This fundamental bound on the performance of non-intrusive load monitoring adversaries is used to develop privacy-preserving policies. Particularly, we devise a load-scheduling policy by maximizing the lower bound on the expected estimation error of non-intrusive load monitoring algorithms.

Smart walkers with admittance controller usually have limited dynamics and low maximal velocity to provide stable and safe behavior. To enable physically challenging training with smart walker control strategies enabling faster dynamics is needed. However, in certain situations, this can lead to instabilities, which further complicates the finding of suitable parameters for the envisioned training functionalities. To overcome these issues, we have introduced an interaction-energy limiter and developed a strategy to automatically determine individual user parameters for an adaptive admittance controller. The energy limiter bounds the controller and training elements to avoid uncomfortable and dangerous situations. These training elements are placed on a 2D map of a training environment. If the user passes these training elements with our smart walker - RoboTrainer - they are triggered. Therefore we call them spatial control actions. We can show that interaction-energy limiter successfully avoids instabilities and dangerous situations when spatial control actions are triggered. The evaluation with 22 users, which used Robo Trainer with and without individualized parameters, successfully demonstrate the benefits of the parameterization method. The presented method could be generally valuable for the implementation of smart walkers in everyday life since it provides a solution for dealing with users with different skills and a solution for safe interaction with smart walkers using high-dynamic control.

In this work, an open-source ROS interface based on KUKAVARPROXY for control of KUKA robots is compared to the commercial closed-source Robot Sensor Interface available from KUKA. This comparison looks at the difference in how these two approaches communicate with the KUKA robot controller, the response time and tracking delay one can expect with the different interfaces, and the difference in use cases for the two interfaces. The investigations showed that the KR16 with KRC2 has a 50 ms response time, and RSI has a 120 ms tracking delay, with negligible delay caused by the ROS communication stack. The results highlight that the commercial inferface is more reliable for feedback control tasks, but the proposed interface gives read and write access to variables on the controller during execution, and can be used for simple motion and tooling control.

The problem of sensor fault identification in engineering systems described by nonlinear dynamic models is considered. To solve the problem, sliding mode observers are used. The feature of the suggested solution is using the reduced order model of the initial system to design sliding mode observer. This allows to obtain sliding mode observer of reduced dimension and extend a class of systems for which sliding mode observer can be constructed.

In this work, a new design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems is presented. By making use of the closed-loop reference model which works as an observer, the developed adaptive ILC method is able to be adopted to deal with the output tracking problem of nonlinear systems without requiring the measurability of system states. In the system, the uncertainties are formed by the product of unknown parameters and state functions that are also unknown as the system states are not available. In order to facilitate the controller design and convergence analysis, the composite energy function (CEF) method is employed, and the accurate tracking task can be realized successfully. The proposed approach extends CEF-based ILC approach sucessfully to output tracking control of nonlinear systems without requiring the system states information and complicated observer design. The effectiveness of the proposed ILC scheme is verified through an illustrative numerical example.

This paper investigates modifier-adaptation schemes based on Gaussian processes to handle plant-model mismatch in real-time optimization of uncertain processes. Building upon the recent work by Ferreira et al. [European Control Conference, 2018], we present two improved algorithms that rely on trust-region ideas in order to speed-up and robustify the approach. The first variant introduces a conventional trust region on the input variables, whose radius is adjusted based on the Gaussian process predictors’ ability to capture the cost and constraint mismatch. The second variant exploits the variance estimates from the Gaussian processes to define multiple trust regions directly on the cost and constraint predictors. These algorithms are demonstrated and compared on a Williams-Otto reactor benchmark problem.

In this study, we aim at realizing the adaptive control system that enables the multi-rotor helicopter to fly autonomously without the tuning by human operator. This paper presents the design of the inner loop feedback control of multi-rotor helicopter based on adaptive PID control. The controller is designed by using the theory of simple adaptive control (SAC) method. Numerical simulation and flight experiment are carried out to verify the efficiency of proposed feedback control.

The Linear Parameter-Varying (LPV) framework has long been used to guarantee performance and stability requirements of nonlinear (NL) systems mainly through the L2-gain concept. However, recent research has pointed out that current L2-gain based LPV synthesis methods can fail to guarantee these requirements if stabilization of a non-zero operating condition (e.g. reference tracking, constant disturbance rejection, etc.) is required. In this paper, an LPV based synthesis method is proposed which is able to guarantee incremental performance and stability of an NL system even with reference and disturbance rejection objectives. The developed approach and the current L2LPV synthesis method are compared in a simulation study of the position control problem of a Duffing oscillator, showing performance improvements of the proposed method compared to the current L2-based approach for tracking and disturbance rejection.

In this paper, the issues of finite-time extended dissipative analysis are investigated for a class of uncertain discrete time switched linear systems. Sufficient conditions for the finite-time boundedness and finite-time extended dissipative performance of the considered systems are proposed by solving some linear matrix inequalities. Using the concept of extended dissipative, we can solve the H∞, L2— L∞, Passivity and (Q, S, R)-dissipativity performance in a unified framework.

In this paper according to complex modeling approach and applying cyber-physical systems is proposed to use control theoretic description of supply chain. Cyber-physical systems are automatic control systems, which used to combine various objects by means of multichannel measuring systems with embedded software in various types of hierarchical structures. We modify multiple-model description of the supply chain by adding the dynamic model of measuring and computing operations control. The extended model allow us to describe the process of receiving the necessary information about the current state of the system. Cyber-physical systems have been applied and the classical methods of optimal program and position control have been transferred and modified for supply chain management (management of organizational and technological systems).

Nowadays, manufacturers increasingly face demand fluctuations while working on a variety of products. To flexibly react to these disturbances, capacity adjustments via flexible working time and staffs are common measures. Within this paper, we focus on a machinery-oriented approach by introducing reconfigurable machine tools into an existing job shop system. As these tools need to be included into the production plan, we tackle the strategic question on how to contemplate hardware with software for a given manufacturing system. To this end, we consider PID as the current standard in capacity control and compare it to two classes of advanced techniques from operator theory and predictive control. While analyzing advantages and disadvantages of these methods, we illustrate differences using operator-based right coprime factorization and model predictive control as representatives of these advanced classes and applying them to a simulated four-workstation job shop system.

In this paper we propose an energy pumping-and-damping technique to regulate nonholonomic systems described by kinematic models. The controller design follows the widely popular interconnection and damping assignment passivity-based methodology, with the free matrices partially structured. Two asymptotic regulation objectives are considered: drive the state to zeroor drive the systems total energyto a desired constant value. In both cases, the control laws are smooth, time-invariant,state-feedbacks. For the nonholonomic integrator we give an almost global solution for both problems, with the objectives ensured for all system initial conditions starting outside a set that has zero Lebesgue measure and is nowhere dense. For the general case of higher-order nonholonomic systems in chained form, a local stability result is given. Simulation results comparing the performance of the proposed controller with other existing designs are also provided.

This paper pursues the construction of a valid strong Lyapunov function for second-order sliding mode algorithms, usually employed for control, observation, and differentiation purposes. The developed solution is based on combining convex representations of such algorithms with the direct Lyapunov method, which leads to stability conditions expressed in the form of linear matrix inequalities. The proposed methodology enlarges the set of gains for which the algorithms are guaranteed stable in prior literature.

Contextual calibration for object detection is a technique where a pretrained network collects attractive false positives during a calibration phase and use this calibration data for further training. This paper investigates the applicability of this method to a vision based onboard sense and avoid system, which requires intruder aircraft detection in camera images. Various landscape and sky backgrounds were generated by Unreal4 3D engine for calibration tests. Contextual calibration is a promising candidate for handling extreme situations which are not covered well in the training data.

In this paper, we propose a real time control method for energy storage system (ESS) to minimize the total cost including the loss caused by load prediction error. To minimize total cost, we consider three types of costs: the energy cost from time-of-use (TOU) tariff, the monthly base cost determined from peak power and battery degradation cost. In order to consider the state-dependent battery degradation cost, we use dynamic programming for the optimal policy. Meanwhile, under the Korea commercial and industrial tariff, minimizing the peak power consumption is critical because it determines the monthly base cost and furthermore may affect for the next 12 months. However, peak shaving is vulnerable to the errors from day-ahead load prediction, and thus conservative strategies are desired. To alleviate the performance loss incurred from the load prediction error, we propose a concept of marginal powerand learn the relationship between the marginal power and the load prediction errors based on the historical data. By exploiting the marginal power, the real-time ESS charging/discharging power becomes close to the optimal offline policy. Our extensive simulation for one year shows that the proposed method reduces the peak power cost by 45%.

The energy flexibility associated with energy consumption must be exploited to accommodate more fluctuating renewable energy. The only solution that enables this without violating privacy concerns is penalty-based control, where penalty signals are designed to give incentives for the consumers to adjust their demand according to the needs of the grid. Designing the penalty signals is a challenging task due to different flexibility potential offered by various energy consuming systems. In this paper, it is shown that the best utilisation of energy flexibility requires individual penalty signals tuned towards the energy flexibility of each consumer. Here, we present a very simple yet novel approach for designing such individual penalty signals for each consumer, such that the value of the energy flexibility is increased, for both the grid operators and the consumers.

During the last few years, servitization arises as a channel for manufacturers to reanimate their business and expand further profit space. Based on an in-depth systematic literature review of 57 papers in the empirical fields, we seek answers for the following question: how servitization strategies influence final value creation through different trajectories? Guided by a proposed framework, we recognize antecedent of servitization process, factors mediating value creation performance and moderator undermining servitized output. Knowledge on servitization is synthesized to provide an enlightening influence path of value creation. It is found that majority of empirical research tends to reveal mediating factor and there is a growing tendency that researchers shift their insights from individual to network, from static to a dynamic and from external to internal.

Under the background of Industry 4.0, this paper studies the single machine scheduling problem by taking into account the time value of money and tardiness penalty. We establish a mixed integer nonlinear programming model for the considered problem, and then convert the above model into a novel integer linear programming model. Regarding the factor of time value of money, we mainly aim to find an optimal or near optimal processing sequence of the jobs such that the total future value of profits is maximized. We employ CPLEX to output optimal solutions for small-size instances. For large-size instances, we propose the genetic algorithm to produce near optimal solutions.

This work investigates the problem of single machine lot scheduling with the objective of minimizing the total completion time of jobs. Each processing lot is with a uniform capacity and is of identical processing time. Jobs assigned to the same lot are all completed at the end time of the lot. The problem has been investigated in Yang et al. (2017) where they tested the performances of classical scheduling rules including Best Fit Random (BFR), Best Fit Non-decreasing (BFN), Next Fit Non-decreasing (NFN), etc, and showed by experiments that BFN outperforms the other rules. In this paper we extend the work of Yang et al. (2017), and propose an improved BFN algorithm named IBFN, which makes a refined adjustment of job assignment, based on the BFN schedule, to reduce the spare space of each lot as much as possible. Numerical results show that the proposed IBFN algorithm behaves better than BFN in all the experimental instances.

With the rapid development of sharing economy, clothing enterprises or suppliers may share their production capabilities between each other via the manufacturing cloud platform. In this paper, we consider the capability allocation of suppliers’ production resources in order to maximize their utilization. We mainly adopt the entropy maximization theory to formulate the objective function. Three aspects of production cost are involved under the fixed budget of each supplier: (i) the unit transportation cost for one batch of raw materials or one unit of product; (ii) the unit processing cost of any supplier to process orders received from customers; (iii) the unit processing cost of any supplier to process orders shared by other suppliers. We use Taylor expansion to reformulate the objective function and construct a mixed-integer second order conic programming model (MISOCP) for the problem. After that we apply CPLEX to solve the model, and finally, the proposed model is employed to a case study for one clothing production enterprise.

In this article the challenge of providing precise runway relative position and orientation reference to a landing aircraft based-on monocular camera and inertial sensor data is targeted in frame of the VISION EU H2020 research project. The sensors provide image positions of the corners of the runway and the so-called vanishing point and measured angular rate and acceleration of the aircraft. Measured data is fused with an Extended Kalman Filter considering measurement noise and possible biases. The developed method was tested off-line with computer simulated data from simulation of the aircraft and the processing of artificial images. This way the image generated noise and the uncertainties in image processing are considered realistically. Inertial sensor noises and biases are generated artificially in the simulation. A large set of simulation cases was tested. The results are promising so completing instrumental landing system and GPS with the estimates can be a next step of development.

In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. This paper addresses the design of an active fault tolerant control scheme that is applied to a wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. Note that, due to the structure of the system and its control strategy, it can be considered as a fault tolerant cooperative control problem of an autonomous plant. The controller accommodation scheme provides the on-line estimate of the fault signals generated by nonlinear filters exploiting the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a data-driven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. This feature of the work, followed by the simpler solution relying on a data-driven approach, can represent the key point when on-line implementations are considered for a viable application of the proposed scheme.

This paper describes the dynamic modelling of a vertical take-off and landing (VTOL) aircraft and shows the flatness of the proposed model. Flat systems have the property that the inputs and the states can be written as functions of a set of the system outputs (called flat outputs) and the derivatives of these flat outputs. The flatness property allows to compute an inverse dynamic model of the given system. This can be used in path planning, nonlinear control and fault detection and isolation. The convertible aircraft presented in this paper uses redundant actuators. The advantage of this design is twofold. Firstly, it is possible to configure actuators to optimize both stationary and fast horizontal flight. Secondly, in case of one actuator failure, it provides sufficient flexibility to reconfigure the actuators in order to land safely. An important contribution of this paper is the demonstration of the flatness property for the proposed dynamical model of the convertible aircraft.

The paper is focused on the leakage detection and localization system for water distribution networks (WDNs). The architecture of the system is briefly presented and its most important modules are discussed in detail. In the second part of the paper, a case study concerning applications of the system to a real WDN located in one of the southern cities in Poland is presented. A pilot version of the system has been running since 2013 in this city, which still shows a high level of performance in leak detection, as well as reasonable efficiency for leak location. The paper ends by emphasizing the merits and limitations of this solution from the water and sewage company’s (WSC’s) perspective.

In this paper, we propose a linearization method of nonlinear control systems by using rough path analysis. The linearization is achieved by the addition of rough signals,which are designed by functions with unbounded variations such as a sinusoidal wave having infinitely-large frequency Therefore, the proposed method is considered to be an extension of the analysis of Lie bracket motions such as the approximation algorithms by Liu and Sussmann in 1990’s, or, the transverse function approach by Morin et al. in 2000’s. Note, however, that, the characteristic feature of the method is to derive controllable linear approximate models for nonholonomic systems even if no Lie bracket motion is generated. Instead, another motions appear based on the similar effects of Wong-Zakai correction terms in stochastic analysis, provided that any stochastic signals are never dealt with in this paper. The validity of the method is confirmed by a few simple examples of linearization problems for nonlinear control systems.

In this paper, we show an example of two-person pursuit-evasion game with infinite horizon where two kinds of capture turn out to differ. One of them, the infimum capture, is a standard kind of capture, while the limit capture, introduced in this paper, is its stronger variant. Both variants of capture have applications, but it is not known in advance whether they always exist simultaneously. The considered example is based on Lion and Man game, i.e. on the game with equal players’ capabilities. The considered space is a compact geodesic space.

This paper deals with the stability analysis of time delay systems based on continuous-time approach. The originality of the present paper relies on the construction of several models for a same time-delay systems using the interconnection of an ordinary differential equation and a transport partial differential equation. The stability analysis is then performed using a Lyapunov functional. These models are constructed in order to first reduce potentially the complexity of the resulting stability conditions. Second several models are build in order to be interpreted as a discretization scheme as the one usually used in the Lyapunov functional. The proposed result can be seen as a generalized (N — M)discretization which consists in both a time-discretization of the delay interval into Msub-intervals, and the projection of the state function within each sub-interval on the Legendre polynomials of degree less than N.The efficiency of this novel approach is illustrated on an academic example.

In this paper, a hybrid control method of pulse width modulation (PWM) and pulse frequency modulation (PFM) is proposed, which can greatly enhance the control performance of the CLLLC resonant converter to output voltage, reduce the ripple of the output voltage and improve the efficiency of the converter. This paper analyzes the theory of hybrid control and introduces the operating principle of the hybrid control method. In addition, the hybrid control method ensures that the power MOSFETs devices realize zero voltage switching (ZVS) and improve the efficiency of converter. Finally, the performance of the proposed hybrid control method is compared with the traditional PFM control and verified by simulation.

This paper presents the design of a fractional order combined model reference adaptive control scheme. This approach is based on the estimates of the plant unknown parameters and the adjusted controller parameters, which are obtained from adaptive laws using the identification error, the control error and the so called closed loop estimation errors. Fractional order indirect algebraic model reference adaptive control and fractional order indirect dynamic model reference adaptive control schemes are also proposed in this paper, which are used for comparison purposes with the combined approach. Boundedness of all the signals in the control schemes is analytically proved, as well as the convergence to zero of the mean values of the control, identification and closed loop estimation errors. Simulation results are presented to show the effectiveness of the proposed control strategies.

In this paper we describe a decentralized protocol that is applied for scheduling and management of energy sharing in heterogeneous systems. Particular system we have in mind comprises of agents with different mobility, energy harvesting and energy sharing capabilities herein called aPads, aFish and aMussels. aPads are surface vehicles that harvest energy and are able to transmit it to both aFishes and aMussels. aFish are underwater vehicles that can transmit energy to aMussels or to other aFish, and receive energy from aPads. aMussels, that can either be attached to the sea-ground or float on the surface, can only receive energy, either from aFish or aPads. For such a system to be able to function for an extended period of time, a decentralized energy management protocol should be designed, whose purpose is to schedule and coordinate charging of agents over time. These decisions depend on the current battery levels of different agents, as well as their positions, and should be done in a decentralized manner. In this paper we apply an energy sharing protocol based on TÆMS framework, originally applied to planning and coordination in aerial-ground robotics systems for search and rescue missions. We refine the obtained plans using the coalition-based metaheuristic for vehicle routing problems. We show execution schedules achieved using the proposed protocol and highlight its advantages and flaws when applied to our underwater scenario.

The paper presents the modelling of a mixed traffic flow in which look-ahead controlled vehicles with a speed control are driven together with conventional vehicles. Since the speed profile of the lookahead control may differ from that of the conventional vehicle, the structure of the traffic flow changes. The paper analyses the impact of vehicles applying look-ahead control strategy on the traffic flow. In a simulation-based analysis it is shown that the fundamental diagram of the traffic network changes. A new mixed-traffic model incorporates the nonlinearity of the traffic dynamics using a polynomial formula, in which the look-ahead cruise control is considered with a parameter-dependent form. Using these nonlinear forms the impact of the look-ahead vehicles on the stability of the traffic is examined through controlled invariant sets. In this analysis the sum-of-squares programming method is applied. The results are demonstrated through simulation examples using the VISSIM simulation environment.

The paper proposes a reconfigurable control design of steering and torque vectoring using a variable-geometry suspension system. Torque vectoring control is based on the independent driving of the wheels. Simultaneously, the steering angle is generated by the variable-geometry suspension system by varying the camber angle of the front wheels. The efficiency of wheel tilting and torque vectoring, and their coordinated actuation are in the focus of the paper. In the analysis a reachable set computation method on the polynomial model of the vehicle based on the trajectory reversing method and the Sum-of-Squares (SOS) programming is proposed. Based on the analysis results a reconfigurable control is designed using the Linear Parameter Varying (LPV) method.

This paper addresses the stability analysis of linear systems subject to a time-varying delay. The contribution of this paper is twofolds. First, we aim at presenting a new matrix inequality, which can be seen as an improved version of the reciprocally convex combination, which provides a more accurate delay-dependent lower bound. When gathering this new inequality with the Wirtinger-based integral inequality, efficient stability conditions expressed in terms of LMI are designed and show a clear reduction of the conservatism with a reasonable associated computational cost. The second original contribution of this paper consists in noting that stability conditions issued from the Wirtinger-based integral inequality depends in an affine manner on the bounds of the delay function and also on its derivative. This allows to refine the definition of allowable delay set and to relax usual convex on the delay function. As a result of this new characterization, the LMI conditions allows obtaining stability regions for slow time-varying delay systems which are very closed to the constant delay case.

In 1968 Fattorini published in SIAM Journal on Control his well-known paper with the title Boundary Control Systems. In this paper he sets up the abstract theory for systems described by partial differential equations with a control at the boundary. The theory as presented is complete, meaning that it could be copied into textbooks and articles without major alternations. In his paper and in these textbooks one finds examples illustrating the use of this abstract concept. However, as we will show, these examples are much older, and are more or less standard within the theory of partial differential equations with an inhomogeneous boundary condition.

To extend inverted decoupling control method to the non-square multivariable time delay system, this paper presents a block inverted decoupling control approach with internal model structure. In this method, the non-square system is divided into two subsystems, and the decoupling structure is composed of two blocks. Each block in the designed structure is applied to each subsystem. With the proposed method, complex matrix computations are avoided, and the designed controller has low order. The stability of closed-loop system with the proposed method is analyzed in the paper. Numerical simulations are performed to illustrate the effectiveness of this methodology.

In order to solve the small ship modeling and path-following control problem of unmanned surface vehicles(USV) under the time-varying navigation environment and vessel speed, an adaptive PF controller based on characteristic model and all-coefficient adaptive control algorithm was designed and implemented. Firstly, the characteristic model was set up, (Differential Global Positioning System)DGPS data and compass data were used to estimate parameters of characteristic model online. Secondly, a course and speed cooperative controller was designed and implemented based on the all-coefficient adaptive control method and characteristic model. Finally, the PF controller for USV was designed and implemented using VF guidance law. And the results of the real ship experiments show that the adaptive path-following controller, which has good controlling performance in the condition of real navigation environment.

Buildings consume a signifcant percentage of the world’s energy resources. The rapid depletion of energy resources, has imparted researchers to focus on energy conservation and wastage. The next generation of intelligent buildings is becoming a trend to cope with the needs of energy and environmental ease in buildings. This advances the intelligent control of building to fulfll the occupants’ need. Intelligent system control for sustainable buildings is dynamic and highly complex.

Techniques on 5G and Internet of things bring a strong potential paradigm shift to wireless communication applications in industrial domain. Hence, there is a strong need for quantitative dependability assessment in these applications. However, with the evergrowing complexity and amount of wireless communication systems, their dependability relevant parameters also increase rapidly. In addition, the deep neural network has advantages on high dimensional data process. Hence, a deep learning-based dependability assessment method is proposed to address the issue, wherein a deep auto-encoder based approach is proposed to reduce data dimension and to obtain the data codes, and DBSCAN is used to cluster these codes. An experimental environment is built for collecting data set on the Multifaces, and a rough classification method is proposed to obtain a superior deep encoder model. Based on the superior model and DBSCAN, the data set are mainly divided into four dependability clusters.

Industry 4.0 brings a more flexible machine scheduling mode via intelligent manufacturing technologies. When each job consists of more than one operation, it may break the traditional constraint that the processing time segments of consecutive operations for a same job cannot overlap. This works studies a flexible job shop scheduling problem where consecutive operations of any job is allowed to be overlapped during processing, while the overlapping induces an extra processing cost. We establish a bi-objective mathematical model for the considered problem, aiming at the minimum makespan and the processing cost induced by the overlapping. We apply the Epsilon-constraint method to solve the model and produce the Pareto front. A brief framework of future research is provided as well.

Multiple-route job shop scheduling problem (MRJSP) is an interesting variation of the job shop scheduling problem such that each job has one or more available processing routes, each of which is of a non-identical number of operations. We investigate the bi-objective MRJSP problem to minimize the makespan and the cost of selected processing routes in this work. Different processing routes of any job are of non-equal processing cost. We establish a bi-objective integer programming model, and devise an epsilon constraint algorithm together with a non-dominated sorting genetic algorithm II (NSGA-II). The epsilon constraint method produces exact solutions for small job instances, while NSGA-II can efficiently solve large job instances. Numerical experiments validate the proposed model and algorithms.

In order to quick response to emergencies such as air crash and natural disaster, satellites on orbit are desirable to fast achieve imaging sequence for real-time tasks. To select targets in hot area for formation flying satellites, two types of “select-by-grade” algorithms are presented to obtain an appropriate imaging sequence for each satellite. First, ground targets are divided into different groups with different grades in terms of their significances. By making analysis on constraints of target locations and satellite performances, the autonomous task planning is treated as a stepwise optimization problem where the objective function is to maximize the overall imaging gain for all satellites. Second, to solve the optimization problem, the aforementioned different grades ground targets are assigned to different satellites by pre-defined distribution schemes. Finally, a simulation example is provided to illustrate the effectiveness of the proposed algorithms.