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Three dimensional digital avatars can be valuable assets in various industrial fields: from telepresence, entertainment, fashion, to wellness and medicine. Accurate geometric representations of a person’s body shape are crucial for extracting reliable anthropometric information applicable in contexts ranging from virtual try-ons to medical-grade applications, where precision can significantly impact outcomes. This paper presents two different automated procedures to perform anthropometrics measurements, both based on an non-rigid, human template mesh registration method that requires no human supervision, centered around a custom human body model. Both procedures were evaluated using datasets from six participants, comparing a fully automated registration with a manual registration performed by a trained operator. The results, analyzed and benchmarked against manual digital tape measurements, confirm that the accuracy of the automated methods aligns closely with ground truth, affirming the reliability of these measurements across diverse body shapes. This highlights the potential of fully automated methods in applications that require precise body measurements.

Drones are aerial robotic platforms that have applications in various domains of work and life. They are well appreciated by young people, especially when using them to play games. Mixing learning and entertainment has proven to be an efficient way of motivating students to better understand scientific curricula. This paper proposes a game development-based approach aiming to promote robotics and artificial intelligence (AI) to prospective engineering students and contribute to their motivation to pursue curricula in these fields. The game considered in this paper is a ”Rock-Paper-Scissors” (RPS) game involving an autonomous drone in interaction with human players. It was implemented in the context of a student project at ESIEA, a graduate school of Engineering in France, in connection with Robotics and AI curricula. The RPS drone has been used as a demonstrator during an open day at ESIEA, as a way of attracting prospective students by showing them how fun and full of learning outcomes projects in these fields can be. The main features of the RPS drone are presented in the paper, as well as elements of the curricula and learning outcomes related to this project. We also provide feedback from prospective students on the open day, and we present a preliminary study of the impact of the proposed approach on student motivation.

In this paper a motion profile design for unmanned aerial vehicles is proposed which method is able to guarantee safe collision-free motion. The motivations of the work are provided by the uncertainties of covered areas by the vehicles, and also the need of high performance fast vehicle motion. The uncertain information on the environment for detecting Conflict areas is processed through clustering and Mahalanobis-distance-based filtering methods. The resulted Conflict areas are involved in the motion design method, which is facilitated through reinforcement learning. This paper shows the application of the method on a drone that moves together with a mobile robot in the same environment. The safe and high performance motion of the drone is illustrated through simulation example.

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples.

Iterative Feedback Tuning (IFT) is a direct, data-driven control technique, that relies on a reference model to capture the desired behavior of the unknown system. The choice of this hyper-parameter is particularly critical, as it potentially jeopardizes performance and even closed-loop stability. This paper aims to explore the suitability of three search methods (grid search, random search, and successive halving) to automatically tune the reference model from data based on a set of user-defined, soft specifications on the desired closed-loop behavior. To compare the three methods and demonstrate their effectiveness, we consider a benchmark simulation case study on the control of a mass-spring-damper system. From our results, successive halving turns out to be the most efficient way to run IFT with automatic reference model selection on a finite budget of time for data collection.

Power-split HEVs can improve their overall fuel economy by applying an appropriate energy management strategy. One of the most popular energy management strategies is model predictive control (MPC) which has attracted a great deal of attention in the HEV research community. In this paper, we focus on the energy management of HEVs and propose a novel energy management system using a new robust predictive control based on almost strictly positive real properties. We also verify that the proposed method can improve fuel efficiency compared with the existing rule-based control through numerical simulations.

This paper investigates the fault-tolerant safe control problem in water network systems in the presence of process and actuator faults. Firstly, based on the established watersafe benchmark, the studied water network is modeled as an interconnected switched system, and a fault-tolerant regulation problem is proposed while ensuring the states of the system remain within safe regions. Secondly, based on the stability theories for interconnected and switched systems, considering the worst cases of leakage and pump faults, a proper state-dependent safety-guaranteed switching law and distributed control policies are designed. Thirdly, the stability and safety of the proposed approach are proved in theory. Finally, simulations are conducted to validate the effectiveness of the proposed approach on a benchmark water transport system.

In Cyber-Physical Systems (CPS) research, the detection of anomalies—identifying abnormal behaviors—and the diagnosis—pinpointing the underlying root causes—are frequently considered separate, isolated tasks. However, diagnostic algorithms necessitate symptoms, i.e., temporally and spatially isolated anomalies, as inputs. Therefore, integrating anomaly detection and diagnosis is essential for developing a comprehensive diagnostic solution for CPS. This paper introduces a method leveraging deep learning for anomaly detection to effectively identify and localize symptoms within CPS. Our approach is validated on both simulated and real-world CPS datasets, demonstrating robust performance in symptom detection and localization when compared to other state-of-the-art models.

Hydrogen production requires renewable energy sources, such as solar energy. It is the lightest atom, and its storage is a real challenge. One of the most promising countries for hydrogen production is Morocco owing to its remarkable solar energy production. In this paper, we will discuss the feasibility and challenges of hydrogen production in a Moroccan household. To approximate reality, this research will focus on a Moroccan house located in a climatic zone characterized by a Mediterranean climate. The research methodology relies on the first hand, on synthetic data of solar energy power to reveal the hydrogen masses that a household can potentially produce during the year 2024. On the second hand, a scheme proposal using Particle Swarm Optimization (PSO) algorithm to automate the flow of electricity in a way to power the house from solar energy or hydrogen tanks, depending on the needs and availability. An Analysis of the system brings an idea about the feasibility in terms of materials and cost. The results show that, with a power of 300 KWh a house can produce from 5.3 to 5.9 Kg. Overall, the feasibility is positive technically but the high cost of materials, water insufficiency and safety matters make decentralized hydrogen production more challenging.

Microgrids have been attracted increasingly attention due to renewable energy integration. Multiple microgrids are often interconnected for benefiting each other. A significant challenge in operating such power systems lies in the uncertainties including intermittence in renewable energy sources and load demands estimation. Sharing state variables in interconnected systems poses additional difficulties. This paper proposes a numerical solution approach to a chance constrained distributed optimisation for interconnected power systems under uncertainty. The inner-outer approximation approach is used for the solution and ALADIN for numerical implementation. An interconnected optimal power flow problem with a three-bus and a four-bus system is solved as a case study.

Entailing a comprehensive examination of energy distribution systems, this study delves into the repercussions of failures in energy distribution channels, with a specific focus on the impact on substitution channels using the Independent Influence Model (ICM). By transcending conventional fault localization approaches, our research uncovers how these failures influence alternatives, expanding our understanding of outage dynamics in energy networks. The results obtained through the application of the ICM offer valuable insights, emphasizing the crucial importance of understanding how failures in energy distribution channels impact the overall resilience of networks. This approach enables a detailed analysis of interdependencies among substitution channels, providing essential guidance for enhancing the resilience of energy networks in the face of uncertainties and disruptions.

We study systems of fractional order differential equations involving the Prabhakar derivative of Caputo type. For commensurate systems we obtain their solutions in closed forms using the eigenvevtors and the eigenvalues of the associated square matrix in the system. We discuss the solutions under the cases where the eigenvalues are distinct, repeated or complex. We present several examples to illustrate the efficiency of the obtained results. For incommensurate systems we apply the Laplace transform to obtain their solutions. As the Prabhakar kernels involve many fractional kernels as particular cases, the obtained results will generalize several existing results in the literature.

A new criterion for evaluating exact values for the maximum angles of A(α)-stability for linear multistep methods is proposed. This criterion gives an equation to be solved for a complex point corresponding to the tangent line from the origin to the root locus curve of the stability boundary. The maximum angle for A(α)-stability sector is then obtained from the tangent point. This criterion is applied to find the A(α)-stability angles for several fractional backward difference formula type (BDF-type) numerical methods for fractional initial value problems.

A common barrier to maximising the altitude of a sounding rocket is the attitude deviation from the vertical fight induced by wind, known as weather cocking. An active stabilisation with canards, which was first proposed and implemented in the previous academic year (2022-23), is significantly improved as follows: a compact fight computer, a robust canard mechanical system, the linear quadratic regulator gain tuned and a new telemetry with extended data monitoring/analysis. The Verification process, which includes the fight mode detection, the wind tunnel experiment, the telemetry test and the attitude estimation test, has been performed. A successful launch in April 2024 shows the effectiveness of the integrated attitude control system and the ground data supporting system.

Robotics is used to illustrate different guidance laws for interception purpose. Ground mobile robots make it possible to show different trajectories followed by an interceptor robot according to various classic laws of interception and according to the maneuver of a target robot. Furthermore, differential game theory allows one to write advanced guidance algorithms with robustness properties such as described by the saddle point inequality or the Nash equilibrium. Robotics is a particularly interesting tool to demonstrate in the context of a pursuit-evasion game that neither player has an interest in deviating from their optimal strategy (guidance law, escape strategy).

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.