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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.