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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Antonios N. Menexis Dimitrios G. Fragkoulis Fotis N. Koumboulis Nikolaos D. Kouvakas Department of Digital Industry Technologies Robotics Automatic Control and Cyber-Physical Systems Laboratory School of Science National & Kapodistrian University of Athens Euboea Greece

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

The problem of providing smart manufacturing units, via supervisor control implemented in FIWARE, is studied. In order to investigate the problem at hand, an indicative manufacturing unit is considered, and the desired safe performance of the unit is achieved via a supervisory control architecture, being implemented in cloud computing environment. The model of the manufacturing unit is developed on the basis of the models of its sensors and actuators, in Discrete Event System (DES) form. The safe behavior of the unit is expressed in the form of nine rules. The rules are translated into nine regular languages that are realized as a set of proper supervisors. The supervisors are analytically determined and conducted in the open platform FIWARE, using all necessary smart data models and platform tools.

关键词： Cloud computing Actuators Web services Computational modeling Supervisory control Data models Sensor systems Intelligent sensors Smart manufacturing Milling machines

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A FIWARE based Input-Output Supervisor control Implementation

A FIWARE based Input-Output Supervisor Control Implementatio...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Antonios N. Menexis Dimitrios G. Fragkoulis Fotis N. Koumboulis Michael G. Skarpetis Department of Digital Industry Technologies Robotics Automatic Control and Cyber-Physical Systems Laboratory School of Science National & Kapodistrian University of Athens Euboea Greece

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In this paper, a FIWARE based control implementation framework for supervisory control of inputoutput models, in Discrete Event System (DES) form, will be introduced, through the case study of an industrial product transfer system having a parametric number of conveyor belts. Each conveyor belt has an actuator (motor) for the belt's clockwise move and a sensor for the detection of the presence of a product. The model of each actuator and the model of each sensor of the belt are expressed in DES form. The input-output relation between consecutive conveyor belts is formally expressed. The satisfactory cooperation between consecutive conveyor belts has been guaranteed by developing a set of two types of supervisors for each cooperative pair. The actuators and the sensors of each belt have been described as smart data models. Finally, an implementation of the proposed supervisor scheme in FIWARE has been proposed.

关键词： Actuators Supervisory control Belts Motors Data models Discrete-event systems Manufacturing automation

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A Two-Step Design for Robust Input-Output Decoupling of a Class of Uncertain and Linear Parameter Dependent systems

A Two-Step Design for Robust Input-Output Decoupling of a Cl...

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Mediterranean Conference on control and Automation (MED)

作者： Michael G. Skarpetis Fotis N. Koumboulis Department of Digital Industry Technologies Robotics Automatic Control and Cyber-Physical Systems Laboratory School of Science National and Kapodistrian University of Athens Euboea Greece

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

This research delves into the intricacies of achieving input-output decoupling for a class of uncertain linear parameter dependent systems. A combined approach is introduced, involving the utilization of a state feedback controller that incorporates both static and time-varying elements. The design of this controller aims to maintain insensitivity to uncertainties. The investigation establishes criteria for determining the feasibility of solving the robust input-output decoupling problem. Under these established conditions, a specific class of state feedback controllers is systematically identified as proficient in effectively addressing the stated problem. The proposed methodology entails deriving the controller and formulating solvability conditions to convert the original Parameter Dependent System (PDS) into an uncertain Linear Time-Invariant (L TI) system. Then an analytical presentation of a static state feedback controller is provided as a mean to resolve the input-output robust decoupling for the uncertain L TI system. The practical applicability of these findings is exemplified through successful implementation in a two-mass translation system, appropriately linked with a hydraulic actuator.

关键词： State feedback Uncertain systems Sufficient conditions Uncertainty Automation Hydraulic actuators control systems

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET Cyber-systems and Robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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Ranking of attributes-comparative study based on data from stylometric domain 26

Ranking of attributes-comparative study based on data from s...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering systems, KES 2022

作者： Zielosko, Beata Stanczyk, Urszula Zabinski, Krzysztof Institute of Computer Science University of Silesia in Katowice B edzinska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering systems (KES 2022)

关键词： Knowledge representation

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Fault Diagnosis based on Behavior and State-Awareness Digital Twin of Key Equipment in Power Supply System

Fault Diagnosis based on Behavior and State-Awareness Digita...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Haifei Zhao, Qin Hao, Jie Tao, Laifa Lu, Chen Beihang University Institute of Reliability Engineering Beijing China Science & Technology on Reliability & Environmental Engineering Laboratory Beijing China Beihang University School of Reliability and Systems Engineering Beijing China AVIC xi'An Flight Automatic Control Research Institute Xi'an China

ISBN: (纸本)9798350337754

With the rapid development of multi-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. Traditional fault diagnosis methods have problems such as a single means of sensory modelling and unbalanced fault data. The rapid development of digital twin technology provides an opportunity to overcome these difficulties. However, how to achieve adaptive updating and how to improve the data-and-model-fusion capabilities are also urgent challenges to be solved. To address the lack of existing research, this paper combines time-frequency domain analysis, physical information neural network based on differential-algebraic equation (DAE-PINN) and transient stability analysis of the power supply system based on a digital twin model with dimensions including fault-behavior-twin and (FBT) fault-state-awareness-twin (FSAT). Ultimately, this paper achieves effective digital twin modelling, fault eigenfeature extraction and high accuracy fault diagnosis, reaching fault data balance and complete state characterization and adaptive update of the diagnostic model. © 2023 IEEE.

关键词： Failure analysis

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Efficient identification of linear, parameter-varying, and nonlinear systems with noise models

arXiv

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arXiv 2025年

作者： Bemporad, Alberto Tóth, Roland IMT School for Advanced Studies Lucca Lucca Italy Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

We present a general system identification procedure capable of estimating of a broad spectrum of state-space dynamical models, including linear time-invariant (LTI), linear parameter-varying (LPV), and nonlinear (NL) dynamics, along with rather general classes of noise models. Similar to the LTI case, we show that for this general class of model structures, including the NL case, the model dynamics can be separated into a deterministic process and a stochastic noise part, allowing to seamlessly tune the complexity of the combined model both in terms of nonlinearity and noise modeling. We parameterize the involved nonlinear functional relations by means of artificial neural-networks (ANNs), although alternative parametric nonlinear mappings can also be used. To estimate the resulting model structures, we optimize a prediction-error-based criterion using an efficient combination of a constrained quasi-Newton approach and automatic differentiation, achieving training times in the order of seconds compared to existing state-of-the-art ANN methods which may require hours for models of similar complexity. We formally establish the consistency guarantees for the proposed approach and demonstrate its superior estimation accuracy and computational efficiency on several benchmark LTI, LPV, and NL system identification problems. © 2025, CC BY.

关键词： Time of arrival

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Robust Triangular Decoupling Velocity Varying controllers for Four Wheel Steering Autonomous Vehicles

Robust Triangular Decoupling Velocity Varying Controllers fo...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

This study explores the challenges of achieving triangular decoupling in a specific group of linear parameter-dependent systems that have uncertain structures, with a particular emphasis on its relevance to four-wheel steering (4WS) vehicles. A new method is presented, combining a state feedback controller that includes static and time-varying elements. The main goal of this controller design is to guarantee triangular decoupling properties and Hurwitz invariability in the face of system uncertainties. The proposed method involves developing the controller and setting solvability criteria to transform the original Parameter Dependent Uncertain System into an uncertain Linear Time-Invariant (LTI) system and subsequently meeting LTI triangular decoupling characteristics. The practical significance of the results is shown by their effective use in controlling the motion of a velocity varying four-wheel steering (4WS) car model with uncertain virtual mass and cornering stiffnesses.

关键词： Linear systems Uncertain systems State feedback Uncertainty Wheels Transforms Reliability Automobiles Time-varying systems Manufacturing automation

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Tuning the feedback controller gains is a simple way to improve autonomous driving performance

arXiv

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arXiv 2024年

作者： Liang, Wenyu Baldivieso, Pablo R. Drummond, Ross Shin, Donghwan Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Dept. of Computer Science University of Sheffield Sheffield United Kingdom

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle’s closed loop dynamics. The aim of this paper is to highlight the limitations of this approach;it is shown that retuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness. © 2024, CC BY.

关键词： Feedback control

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Nonparametric control Koopman Operators

arXiv

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arXiv 2024年

作者： Bevanda, Petar Driessen, Bas Iacob, Lucian Cristian Sosnowski, Stefan Tóth, Roland Hirche, Sandra Chair of Information-oriented Control TU München Germany Control Systems Group TU/e Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

This paper presents a novel Koopman (composition) operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictionary or input parametrizations. By establishing fundamental equivalences between different model representations, we are able to close the gap of control system operator learning and infinite-dimensional regression, enabling various empirical estimators and the connection to well-understood learning theory in RKHSs under one unified framework. As a consequence, our proposed framework allows for arbitrary accurate finite-rank approximations in infinite-dimensional spaces and leads to finite-dimensional predictors without apriori restrictions to a finite span of functions or inputs. To enable applications to high-dimensional control systems, we improve the scalability of our proposed control Koopman operator estimates by utilizing sketching techniques. Numerical experiments demonstrate superior prediction accuracy compared to bilinear EDMD, especially in high dimensions. Finally, we show that our learned models are readily interfaced with linear-parameter-varying techniques for model predictive control. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

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