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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Lei Cao Chuanguo Chi Erhan Yumuk Clara-Mihaela Ionescu Guo-Ping Liu Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electromechanical Systems and Metal Engineering Dynamical Systems and Control Research Group Ghent University Ghent Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Center for Control Science and Technology Southern University of Science and Technology Shenzhen P. R. China

In this paper, the coordinated control problem for a class of distributed networked multi-agent systems (NMASs) is studied. An event-triggered predictive coordinated control (ETPCC) scheme is proposed to compensate for the communication delay between controllers as well as to reduce the communication frequency. With the aim of reducing the frequency of data transfer among controllers and saving resources, a distributed event-triggered condition is designed to decide when to transfer information. Further, a cost function using predicted values is proposed to measure the cooperative performance among the agents, and the required control increment for each agent is obtained by minimizing the cost function. Simulation and experimental examples show that the proposed ETPCC strategy works effectively.

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Machine Learning in Predicting the Impact of Filter Parameters on the Accuracy of Biosignal Measurement

Machine Learning in Predicting the Impact of Filter Paramete...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Raisa K. Uskenbayeva Abu-Alim E. Ayazbay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Institute of Automation and Information Technologies Vice-Rector Satbayev University Almaty Kazakhstan Electronic engineering Almaty University of Power Engineering and Telecomunications Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this scientific article under consideration, a machine learning model has been developed that allows predicting the impact of filter parameters on the accuracy of measuring a biosignal - an ECG (electrocardiography) signal. For the first time, quantitative results have shown the degree of influence and the error introduced into the measured biosignal and described through dependence. The results of assessing the obtained relationship of indicators demonstrate an inverse correlation relationship that characterizes a decrease in the accuracy of measurement at the output of a high-order digital filtering system. The machine learning model is based on the linear regression method the dependence of which is approximated by the method based on least squares and visualized using a scattergram. The adequacy of the developed machine learning model was confirmed by calculating the coefficient of determination and checking its statistical significance. The developed model can be used to create new or improve existing biomedical data processing systems to increase the reliability of the results.

关键词： Accuracy Shape Filtering Biological system modeling Machine learning Biomedical measurement Electrocardiography Distortion Distortion measurement Digital filters

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Population-Dynamics-Assisted Coalitional Model Predictive control for Parabolic-Trough Solar Plants

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IFAC-PapersOnLine 2023年第2期56卷 7710-7715页

作者： Ana Sánchez-Amores Juan Martinez-Piazuelo José M. Maestre Carlos Ocampo-Martinez Eduardo F. Camacho Nicanor Quijano Systems and Automation Engineering Department University of Seville Spain Automatic Control Department Universitat Politècnica de Catalunya Spain Departamento de Ingeniería Eléctrica y Electrónica Universidad de los Andes Colombia

This paper proposes a coalitional model predictive control method for temperature regulation in parabolic-trough solar fields. The global optimization problem is divided into a set of local subproblems that will be solved in parallel by a set of coalitions. However, these local (smaller) problems remain coupled by a common global resource constraint. In this regard, we present a population-dynamics-assisted resource allocation approach to fully decouple the local optimization problems. By doing this, each coalition can address its corresponding optimization problem without relying on the solutions of the other coalitions. To illustrate the proposed methodology, we provide simulation results for a 100-loop parabolic-trough solar collector field.

关键词： Model predictive control coalitional control population dynamics distributed solar collector field

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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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

作者： Yiqi Liu Zhi Li Hongtian Chen Key Laboratory of Autonomous Systems and Networked Control Ministry of Education The School of Automation Science & Engineering South China University of Technology Guangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial Intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor.

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Discretizing Continuous Action Space With Unimodal Probability Distributions for On-Policy Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 11285-11297页

作者： Yuanyang Zhu Zhi Wang Yuanheng Zhu Chunlin Chen Dongbin Zhao Department of Control Science and Intelligence Engineering School of Management and Engineering and the Laboratory of Data Intelligence and Interdisciplinary Innovation Nanjing University Nanjing China Department of Control Science and Intelligence Engineering School of Management and Engineering and the Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

For on-policy reinforcement learning (RL), discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient (PG) estimator. In this article, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy RL algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the PG estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process.

关键词： Task analysis Probability distribution Aerospace electronics Reinforcement learning Gaussian distribution Technological innovation Learning systems

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Guide for Stabilization and Heuristic Optimization of Second-order systems controlled by P/I-delayed controllers

Guide for Stabilization and Heuristic Optimization of Second...

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International IEEE Conference on Intelligent systems, IS

作者： Martin Strmiska Libor Pekař José Mario Araújo Department of Automation and Control Engineering Tomas Bata University Zlin Czechia Department of Automation and Systems Federal Institute of Education Science and Technology of Bahia Salvador Brazil

ISBN: (纸本)9781665492768

The present paper shows the procedure for the stabilization and subsequent optimization of second-order systems using a proportional plus integral delayed controller. Stabilizing closed-loop control systems using a controller comprehends several techniques to increase the control circuit's stability. Even in some simple second-order plants, the classical proportional integral controller cannot be applied to pursue closed-loop stability. However, the design requires constant reference tracking capability, for example. Moreover, if there is a time delay in the feedback loops inside a controlled system, the system has an infinite spectrum – infinitely many roots (poles); this degrades the control's quality, especially in terms of stability, robustness, and oscillation response. For these reasons, it is also necessary to optimize the transient response of the system. The main objective of this paper is to provide a guide for optimal control of second-order systems controlled by a proportional integral-delayed controller. Some examples are chosen as benchmarks to verify the effectiveness of the stabilization and subsequent heuristic optimization. In future research, we would like to focus on implementing selected modern optimization algorithms for solving non-smooth, non-convex, and non-Lipschitz optimization problems.

关键词： Asymptotic stability Transient response control systems Search problems Stability analysis Robustness Circuit stability

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Multi-Objective Optimal control of Wastewater Treatment Processes Constrainted by Energy-Saving and Effluent Qualities

Multi-Objective Optimal Control of Wastewater Treatment Proc...

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Robotics, control and automation engineering (RCAE), International Conference on

作者： Jing Zhang Haoming Yan Hongtian Chen Shuiqing Xu Yiqi Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering South China University of Technology Guangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China College of Electrical Engineering and Automation Hefei University of Technology Hefei China

Considering the high energy consumption problem in the wastewater treatment process, this paper proposes a multi-objective optimization control scheme based on NSGA-II algorithm. The proposed scheme can not only ensures meet effluent quality requirements but also can achieves energy cost savings. This method takes the aeration energy consumption and pumping energy consumption of the system as the optimization goal with the effluent water quality being the constraint condition. At the same time, the adaptive fuzzy PID controller is designed to ensure the control accuracy and stability of the controlled system. The results of simulation experiments validated in benchmark simulation platform, BSM1, show that, compared with PI control and single-objective optimization control, the proposed optimization scheme in this paper can achieve the desired quality of the effluent water withmore energy-saving.

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Oscillation Suppression Method based on Power control Optimization of the Wind Farms 3

Oscillation Suppression Method based on Power Control Optimi...

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3rd IEEE International Power Electronics and Application Conference and Exposition, PEAC 2022

作者： Yang, Jiaqi Wang, Shaojun Li, Zhen Tian, Xu Liu, Bin Liu, Xiangdong Beijing Institute of Technology School of Automation Beijing China Co.Ltd Bayannur Power Supply Bureau Inner Mongolia China State Key Laboratory for Intelligent Control and Decision of Complex Systems Beijing China Beijing University of Mining and Technology Beijing Electric Engineering and Automation Beijing China Tsinghua University Department of Electrical Engineering Beijing China

ISBN: (数字)9781665491419

ISBN: (纸本)9781665491419

Oscillation issues in the grid-connected wind farm system can severely deteriorate the power system security and lead to serious damage. Based on the impedance analysis method, many oscillation suppression methods have been proposed for reshaping the impedance characteristics of system to avoid the risk of oscillations. However, modifying converters' controllers to reshape the wind turbines (WTs) impedance and usage of additional damping devices for reshaping the grid impedance both are time-consuming and costly. Therefore, based on the sequence impedance model of the grid-connected wind farm system, this paper proposes a novel oscillation suppression method by optimizing the power control of wind farm. The system stability margin can be improved by modifying the number of online WTs and the output power of each WT to reshape the impedance of wind farm, under the precondition that the output power demand of wind farm is satisfied. Then, the intelligent particle swarm optimization (PSO) algorithm is used to solve the optimization approach to obtain the number of online WTs with optimal stability margin. Finally, the simulation confirms the effectiveness of the proposed oscillation suppression method. © 2022 IEEE.

关键词： System stability

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Flexible Active Safety Motion control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

arXiv

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arXiv 2024年

作者： Liu, Jinhao Yang, Jun Mao, Jianliang Zhu, Tianqi Xie, Qihang Li, Yimeng Wang, Xiangyu Li, Shihua The School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom The College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China

A flexible active safety motion (FASM) control approach is proposed for the avoidance of dynamic obstacles and the reference tracking in robot manipulators. The distinctive feature of the proposed method lies in its utilization of control barrier functions (CBF) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, thereby offering flexibility and active safety for robot manipulators in dynamic environments. First, discrete-time CBFs are employed to formulate the novel flexible CBFSC with dynamic decay rates for robot manipulators. Following that, the model predictive control (MPC) philosophy is applied, integrating flexible CBFSC as safety constraints into the receding-horizon optimization problem. Significantly, the decay rates of the designed CBFSC are incorporated as decision variables in the optimization problem, facilitating the dynamic enhancement of flexibility during the obstacle avoidance process. In particular, a novel cost function that integrates a penalty term is designed to dynamically adjust the safety margins of the CBFSC. Finally, experiments are conducted in various scenarios using a Universal Robots 5 (UR5) manipulator to validate the effectiveness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Towards Energy Efficient control for Commercial Heavy-Duty Mobile Cranes: Modeling Hydraulic Pressures using Machine Learning

arXiv

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arXiv 2023年

作者： Taheri, Abdolreza Pettersson, Robert Gustafsson, Pelle Pajarinen, Joni Ghabcheloo, Reza Control Systems R&D HIAB Hudiksvall Sweden Faculty of Engineering and Natural Sciences Tampere University Tampere Finland Department of Electrical Engineering and Automation Aalto University Espoo Finland

A sizable part of the fleet of heavy-duty machinery in the construction equipment industry uses the conventional valve-controlled load-sensing hydraulics. Rigorous climate actions towards reducing CO2 emissions has sparked the development of solutions to lower the energy consumption and increase the productivity of the machines. One promising solution to having a better balance between energy and performance is to build accurate models (digital twins) of the real systems using data together with recent advances in machine learning/model-based optimization to improve the control systems. With a particular focus on real-world machines with multiple flow-controlled actuators and shared variable-displacement pumps, this paper presents a generalized machine learning approach to modeling the working pressure of the actuators and the overall pump pressures. The procedures for deriving reaction forces and flow rates as important input variables to the surrogate models are described in detail. Using data from a real loader crane testbed, we demonstrate training and validation of individual models, and showcase the accuracy of pressure predictions in five different experiments under various utilizations and pressure levels. © 2023, CC BY.

关键词： Energy utilization

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