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H∞ fault-tolerant fuzzy intermittent control for nonlinear hyperbolic PDE systems with multiple delays and actuator failures

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ISA Transactions 2025年 156卷 39-60页

作者： Zhang, Xu Gao, Xuan Wang, Zi-Peng Luo, Biao Han, Ke-Zhen Yan, Xue-Hua School of Automation Central South University Changsha410083 China School of Electrical Engineering University of Jinan Jinan250022 China School of Information Science and Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System and Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing100124 China

This study presents an H∞ fault-tolerant fuzzy intermittent control approach for the nonlinear hyperbolic partial differential equation (PDE) systems with multiple delays and actuator failures (MDAFs). Firstly, the nonlinear hyperbolic PDE systems with MDAFs are characterized by the Takagi–Sugeno (T–S) fuzzy delayed hyperbolic PDE model. Next, by employing the Lyapunov direct method, this paper demonstrates the robust exponential stability using spatial linear matrix inequalities (SLMIs) based on a new switching Lyapunov functional (LF). Furthermore, the H∞ fault-tolerant fuzzy intermittent control issue for nonlinear hyperbolic PDE systems with MDAFs is transformed into the LMI feasibility problem to deal with the SLMIs. Lastly, the feasibility of the constructed control strategy is demonstrated by two illustrative examples. © 2024 International Society of automation

关键词： Lyapunov functions

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Swarm-Based Dynamic Coverage of Multi-ASV systems in the Presence of Measurement Noises

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7237-7250页

作者： Liu, Lu Jiao, Shijian Han, Bing Li, Tieshan Peng, Zhouhua Dalian Maritime University School of Marine Electrical Engineering Dalian116026 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China Shanghai Ship and Shipping Research Institute National Engineering Research Center of Ship & Shipping Control System Shanghai200135 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper is concerned with the dynamic coverage of multiple autonomous surface vehicles (ASVs) in the presence of static and dynamic obstacles, limited communication distance, and measurement noises. A modular control method including planning, guidance, and control is proposed. At the planning level, a swarm-based dynamic coverage strategy is developed to achieve the coverage of a given domain. Both the collision avoidance and connectivity preservation issues are solved by using artificial potential fields. At the kinematic level, novel adaptive line-of-sight guidance laws are presented based on cascade extended state observers (CESOs), such that accurate guidance signals are given by compensating for time-varying sideslip angle and suppressing measurement noises. At the kinetic level, two CESOs are developed to estimate the internal model uncertainties and external environmental disturbance, and antidisturbance kinetic control laws are designed subsequently. The proposed CESOs overcome the noises sensitivity problem of classical observers, and thus facilitate the practical implementation under measurement noises. The input-to-state stability of the closed-loop system is confirmed by the Lyapunov theory and cascade stability theory. Simulation and hardware-in-the-loop experiment results are provided to demonstrate the effectiveness of the proposed swarm-based dynamic coverage method. © 1967-2012 IEEE.

关键词： Velocity measurement

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Enhancing Online Load Model Parameter Identification with Deep Reinforcement Learning: A DDPG-Based Approach

Enhancing Online Load Model Parameter Identification with De...

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International Conference of electrical, Electronic and Networked Energy systems, EENES 2024

作者： Li, Zihao Liu, Xingwei Huang, Yupeng Hu, Jianxiong Tang, Yi School of Electrical Engineering Southeast University Nanjing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology Beijing China China Electric Power Research Institute Beijing China

ISBN: (纸本)9789819620791

To cope with the complex dynamic characteristics brought about by the sharp increase in penetration of various power electronic devices, online dynamic security analysis in modern power systems has put forward higher requirements for the accuracy of simulation models. Traditional methods for load model parameter identification (PI) struggle with model complexity, computational pressure, and low generalization, which can hinder their effectiveness in online applications. To overcome these challenges, this paper proposes a novel online load model PI method utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm. Initially, a composite load model incorporating various distributed energy resources (DERs) is developed as the model basis. Subsequently, a DDPG-based framework for online load model PI is designed, featuring meticulously crafted interface functions to improve both convergence and generalization capabilities. Case studies on the EPRI-36 buses test system demonstrate the proposed method's efficiency and accuracy compared to traditional heuristic optimization methods. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep reinforcement learning

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Noncooperative Game-Based Composite Voltage Regulation for Microgrid With Switching Topologies and Prescribed Performance

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 1720-1731页

作者： Liu, Yuezhi Chen, Yong Zhang, Longjie Wang, Hongru Hafez, Esam University of Electronic Science and Technology of China School of Automation Engineering Sichuan Chengdu611731 China Sichuan Province Engineering Technology Research Center for Electric Vehicle Driving System and Safety Sichuan Chengdu611731 China College of Electrical and Information Engineering Zhengzhou niversity of Light Industry Zhengzhou450002 China Aswan University Faculty of Energy Engineering Aswan81528 Egypt

In this paper, the voltage regulation issue of the microgrid with the switching topologies, nonlinear coupling and prescribed performance is investigated. Firstly, to remove the requirement of the global topology information, a noncooperative game layer with the fully distributed Nash equilibrium seeking (FDNES) approach is devised. In this study, the noncooperative game is performed over the switching communication topologies. Furthermore, the voltage regulation layer is formulated, which integrates the adaptive coupling observer (ACO) and prescribed performance based composite voltage regulation (PPCVR) methods. Specifically, in the ACO, an adaptive observer gain is devised to attenuate the damaging peak dynamics. For the PPCVR, the prescribed performance function based dynamic surface control strategy is formulated such that the voltage tracking error can be restrained according to the specified constraints, which will enhance the voltage regulation performance. Additionally, the PPCVR utilizes the coupling estimation by the ACO to compensate the nonlinear coupling dynamics. It is shown that the signals of the closed-loop system are semi-global uniformly and ultimately bounded (SGUUB). Finally, comparative studies and hardware-in-loop experiment by RT-LAB platform are performed to illustrate the effectiveness of the proposed voltage regulation strategy. © 2013 IEEE.

关键词： Microgrids

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A Novel Integrated Energy Management Strategy of Energy Storage system for a Pure Electric-Driven Mining Hydraulic Excavator

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Chinese Journal of Mechanical Engineering (English Edition) 2025年第1期38卷 1-17页

作者： Qin, Tao Li, Yunhua Quang, Long Yao, Yu Yang, Liman School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Tianma Intelligent Control Technology Co. Ltd. Beijing101399 China Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Tai Yuan030024 China

Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs. However, pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power, leading to significant fluctuations in the power demand of the grid, which in turn affects production. To solve the above problem, a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object. Moreover, supercapacitors and DC/DC converter, as the energy storage system (ESS) adjust the output power of the grid and recover the braking kinetic energy of the swing platform. Subsequently, a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably. Specifically, the working modes of the ESS are determined by the DC bus voltage and state of charge (SOC) of the supercapacitor. Next, the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules. In addition, a digital prototype of the excavator was verified using an original machine test. The performance of the different strategies and driven systems were analyzed using digital prototypes. The results showed that, compared with traditional excavators with diesel engines, the operational cost of the developed excavators was reduced by 54.02%. Compared to pure electric-driven excavators without an ESS, the peak power of the grid for the developed excavators was reduced by 10%. This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS. © The Auth

关键词： Predictive control systems

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Finite-Time Consensus of Second-Order Multiagent systems With Input Saturation via Hybrid Sliding-Mode Control

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IEEE Transactions on automation Science and Engineering 2025年 22卷 14623-14632页

作者： Jiang, Xiaowei Jiao, Ranran Li, Bo Zhang, Xianhe Yan, Huaicheng China University of Geosciences School of Automation Wuhan430079 China Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Anhui University of Finance and Economics School of Finance Bengbu233030 China Hubei Normal University School of Electrical Engineering and Automation Huangshi435002 China Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China

This paper addresses the finite-time consensus (FTC) issue for second-order multi-agent systems (MASs) with nonlinear disturbances. To tackle the challenges posed by increasingly complex communication environments, an innovative integral sliding-mode surface is designed using event-triggered control. Furthermore, the study explores communication constraints between the leader and followers, employing impulsive control to facilitate communication only at specific intervals. A novel hybrid integral sliding-mode control protocol is advanced for the second-order MASs, which effectively eliminates the "Zeno phenomenon" and notably reduces both communication frequency and energy consumption, thereby enhancing overall communication efficiency. Ultimately, the efficacy of the put forward protocol is verified through simulation examples including comparison experiment. © 2004-2012 IEEE.

关键词： Protocols Sliding mode control Event detection Multi-agent systems Costs Training Topology Switches Sufficient conditions Stability analysis

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An online self-organizing radial basis function neural network based on Gaussian Membership

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Applied Intelligence 2025年第6期55卷 1-17页

作者： Jia, Lijie Li, Wenjing Qiao, Junfei Zhang, Xinliang School of Electrical Engineering and Automation Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Henan Polytechnic University Jiaozuo454003 China Faculty of Information Technology Beijing University of Technology No. 100 Pingleyuan Chaoyang District Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System No. 100 Pingleyuan Chaoyang District Beijing100124 China

Radial basis function neural network (RBFNN) is one of the most popular neural networks, and an appropriate selection of its structure and learning algorithms is crucial for its performance. Aiming to alleviate the sensitivity of the RBFNN to its parameters and improve the overall performance of the network, this study proposes a Gaussian Membership-based online self-organizing RBF neural network (GM-OSRBFNN). First, the Gaussian Membership is introduced to enhance network insensitivity to network parameters and used as a similarity metric to indicate the similarity between the sample to a hidden neuron and that between hidden neurons. Second, the similarity metric is used to design the neuron addition and merging rules to achieve a self-organizing network structure, and error constraints are introduced to the neuron addition rule;also, the noisy neuron deletion rule is defined to make the network structure more compact. In addition, an online fixed mini-batch gradient algorithm is used for online learning of network parameters, which can guarantee fast and stable convergence of the network. Finally, the proposed GM-OSRBFNN is tested on common nonlinear system modeling problems to verify its effectiveness. The experimental results show that compared to the existing models, the GM-OSRBFNN can achieve competitive prediction performance with a more compact network structure, faster convergence speed, and, more importantly, better insensitivity to network parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Radial basis function networks

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Digital Twin-Based Obstacle Avoidance for Unmanned Aerial Vehicles Using Feedforward-Feedback Control

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IEEE Transactions on Vehicular technology 2025年

作者： Yang, Hongjiu Sun, Shaopeng Xia, Yuanqing Li, Peng Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China Beijing Institute of Technology School of Automation Beijing100081 China

In this paper, a digital twin (DT) system is designed for an unmanned aerial vehicle (UAV) in obstacle avoidance to enable autonomous navigation and control. The DT system for the UAV is composed of four components: Virtual space, physical space, application services and data processing. To reduce computational burden and sensor requirement on the UAV, a trajectory planning framework is designed for obstacle avoidance in the DT system. Multiple feasible trajectories are generated offline by an EGO-Planner algorithm in the virtual space. The optimal feasible trajectory is selected by trajectory length and completion time of the multiple feasible trajectories. To improve control performance, a feedforward-feedback control strategy is proposed for the UAV using the DT system. In the virtual space, feedforward control input of the physical UAV is obtained offline by tracking the optimal feasible trajectory using a model predictive controller. The model predictive controller is also used as feedback control input for the physical UAV in the physical space. Experimental results show effectiveness of the autonomous navigation and control method for a quadrotor using feedforward-feedback control based on the DT system. © 2025 IEEE.

关键词： Predictive control systems

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Distributed Proportional-Integral Algorithms for Multiple Coalition Games under Limited Communication Resources

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IEEE Transactions on Signal and Information Processing over Networks 2025年 11卷 450-459页

作者： Liu, Jiaxun Wang, Dong Liu, Jiashuo Dong, Xiwang Ludong University School of Mathematics and Statistics Science Yantai264025 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China Optimization for Industrial Equipment of Ministry of Education Key Laboratory of Intelligent Control China Beihang University Institute of Unmanned System Institute of Artificial Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

This paper studies the algorithm design for multiple coalition games under limited communication resources, in which the players in the same coalition cooperatively optimize the summation of cost functions in this coalition and do not care about the costs of other coalitions. To address this game, we develop a distributed proportional-integral algorithm based on the coalition estimate strategy and the proportional-integral principle. Furthermore, when the communication resource is concretely quantified by bit rates in communication channels, we propose a coding-decoding-based distributed proportional-integral algorithm based on the distributed proportional-integral algorithm and coding-decoding rules for seeking the Nash equilibrium of multiple coalition games. It proves that both algorithms linearly and precisely converge to the Nash equilibrium in spite of limited communication resources. Then, the necessary and sufficient condition for the linear convergence of the proposed algorithm about the requirement of bit rates is presented. Moreover, the relationship between the bit rate and the convergence speed of the proposed algorithm is also theoretically explained. Lastly, the simulation in formation problems of unmanned vehicle swarms is presented to demonstrate the effectiveness of proposed algorithms. © 2015 IEEE.

关键词： Cost functions

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Optimal dispatch of integrated energy system considering time-space dimensional demand response based on time-of-use electricity price

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Frontiers in Energy Research 2025年 13卷

作者： Cheng, Tingli Zeng, Liqiang Tang, Hao Zhang, Yingying School of Electrical and Automation Engineering Hefei University of Technology Hefei China State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Electrical Engineering Chongqing University Chongqing China

Demand response has significant potential for enhancing energy utilization, thereby contributing to the advancement of integrated energy systems (IES). In this paper, an optimal IES dispatch model fully incorporating flexible loads on the demand sides is established. Firstly, based on time-of-use (ToU) electricity pricing, a flexible two-dimensional integrated demand response (IDR) model is presented, which is characterized by considering tightly spatial-temporal coupling characteristics of different loads. Subsequently, a day-ahead dispatch model for IES, incorporating ToU-based IDR, is developed to minimize the total operational cost. In this model, the optimal ToU pricing scheme is determined by comparing their economic efficiencies. The Particle Swarm Optimization (PSO) algorithm is then applied to solve the complex dispatch problem. Finally, the case study is conducted and the comparative results confirm that: 1) implementation of the IDR in time-space dimension can flatten the loads curves;2) the time-space IDR significantly promotes the economic benefits of IES dispatch;3) the proposed dispatch model is able to coordinate and optimize various types of energy and flexible loads. Copyright © 2025 Cheng, Zeng, Tang and Zhang.

关键词： Demand response

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