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Data-driven output regulation control for constrained linear systems

science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Resilient time-varying formation optimal tracking for heterogeneous multi-agent systems with coupled constraints

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IEEE Transactions on automation science and engineering 2025年 22卷 15274-15285页

作者： Su, Lingfei Lin, Zhi Hua, Yongzhao Shi, Zhexin Dong, Xiwang Lu, Jinhu Wang, Danwei Beihang University School of Artificial Intelligence Institute of Artificial Intelligence Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Shenyuan Honors College Beijing100191 China Beijing100191 China Science and Technology on Aircraft Control Laboratory Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Zhongguancun Laboratory Beijing100094 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

Formation constrained optimal tracking problems for heterogeneous multi-agent systems under Byzantine attacks are studied. The objective of the honest agents, unaffected by Byzantine attacks, is to find optimal trajectories that minimize the cumulative local cost functions of all honest agents, while simultaneously satisfying the cumulative local inequality constraints and maintaining the formation configuration. A distributed resilient formation tracking controller utilizing preview control and primal-dual strategy is proposed without requiring each agent to know which agents are affected by Byzantine attacks. The performance of the proposed algorithm is analyzed in terms of the upper bounds of dynamic regret and constraint violations. Numerical simulations and experiments, involving one unmanned aerial vehicle and four unmanned ground vehicles, are performed to verify the effectiveness of the obtained results. © 2004-2012 IEEE.

关键词： Cost functions

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Spatial Coordination of Multiple Nonholonomic Agents With Sensory Connectivity Maintenance

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3425-3435页

作者： Chen, Xi Chen, Meimin Zhu, Lijun Chai, Li Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Shandong Huayu University of Technology School of Electrical Engineering Shandong253034 China School of Artificial Intelligence and Automation Wuhan430074 China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

This article aims to propose a general control strategy for coordination of multiple nonholonomic agents in three dimensional space. For real-world applications, since the field sensor equipped on the mobile agents for local information detection and estimation has some limited detecting range, it is necessary to guarantee that the nearby agents must stay within this range of the onboard sensor. This is called sensory connectivity maintenance. A function termed as coordination function with sensory connectivity maintenance (CFSCM) is defined to describe the performances of the coordination as well as the sensory connectivity status between the agents. Then, a general control strategy is designed based on the proposed CFSCM for spatial coordination of multiple nonholonomic agents with sensory connectivity maintenance. Moreover, the applications of the proposed control strategy for formation with omnidirectional sensors and flocking with directional sensors are shown, respectively. Finally, some numerical examples are conducted to validate the theoretical analysis. © 2013 IEEE.

关键词： Maintenance Robot sensing systems Kinematics Vectors Electronic mail Aerospace electronics Three-dimensional displays System dynamics Robot kinematics Multi-agent systems

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Distributed Leaderless Optimal Output Consensus of Heterogeneous Multi-Agent Systems

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Journal of Systems science & Complexity 2025年第2期38卷 671-690页

作者： ZHANG Liping ZHANG Huanshui XIE Lihua College of Electrical Engineering and Automation Shandong University of Science and Technology School of Control Science and Engineering Shandong University School of Electrical and Electronic Engineering Nanyang Technological University

The distributed optimal output synchronization problem for the leaderless heterogeneous multi-agent system with a general global cost function is investigated for the first time by linear quadratic(LQ) optimal control theory. Conventional algorithms for heterogeneous systems are quite complex, requiring the design of a virtual reference generator and the solving of regulation *** paper presents a novel distributed asymptotically optimal controller by incorporating the design of distributed observer and feedforward controller. A general form of the distributed controller is obtained by solving an augmented algebraic Riccati equation, which is parallel to classical optimal control theory. The optimal topology is an arbitrary directed graph containing only a spanning tree. It is shown that the proposed algorithms outperform the traditional consensus methods in the convergence speed by selecting proper observer gain matrices, and eliminate the reliance on the nonzero eigenvalues of Laplacian matrix. Simulation example further demonstrates the effectiveness of the proposed scheme and a faster superlinear convergence speed than the existing algorithm.

关键词： Distributed feedforward control heterogeneous systems leaderless output consensus optimal control

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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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Enhancing Unmanned Ground Vehicle Evasion Through Generative Adversarial Imitation Learning in UAV Pursuit Scenarios

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Guidance,Navigation and control 2025年第1期05卷 149-154页

作者： Shuai Hao Zhaoyu Zhang Mengzhen Huo Chen Wei School of Automation Science and Electrical Engineering Beihang University National Key Laboratory of Aircraft Integrated Flight Control Beihang University

This study focuses on enhancing the evasion capabilities of unmanned ground vehicles(UGVs) using Generative Adversarial Imitation Learning(GAIL). The UGVs are trained to evade unmanned aerial vehicles(UAVs). A decision-making neural network has been trained via GAIL to refine evasion strategies with expert demonstrations. The simulation environment was developed with OpenAI Gym and calibrated with real-world data for the improvement of accuracy. The integrated platform including the proposed algorithm was tested in flight experiments. Results showed that the UGVs could effectively evade UAVs in the complex and dynamic environment.

关键词： Unmanned ground vehicles unmanned aerial vehicles imitation learning flight experiment

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Federated Accelerated Deep Reinforcement Learning for Multi-Zone HVAC control in Commercial Buildings

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IEEE Transactions on Smart Grid 2025年第3期16卷 2599-2610页

作者： Xia, Yihan Wang, Xinli Yin, Xiaohong Bo, Wanlin Wang, Lei Li, Shaoyuan Li, Kang Shandong University School of Control Science and Engineering Jinan250100 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao266101 China University of Leeds School of Electronic and Electric Engineering LeedsLS2 9JT United Kingdom

Deep Reinforcement Learning (DRL) has demonstrated a promising prospect in optimizing Heating, Ventilation, and Air Conditioning (HVAC) systems to minimize energy costs and improve thermal comfort without requiring the knowledge of building thermal dynamic models. However, DRL training can be difficult to converge due to its long exploration, especially in multi-agent scenarios. To address these concerns, we propose a novel Federated Accelerated Multi-Agent DRL (FA-MADRL) algorithm for HVAC control in commercial buildings with multi-zone offices. To be specific, we reformulate the optimal control problem of indoor temperature, CO2 concentration and humidity in multi-zone HVAC as a Markov Decision Process (MDP). Then, we establish a MADRL framework for HVAC system control and utilize a federated learning (FL) mechanism to accelerate the convergence during real-time deployment. Experimental studies have been carried out on a TRNSYS-based commercial building HVAC system with multiple zones. The results demonstrate the superiority of our proposed algorithm, with improved convergence speed, reduced energy consumption, and satisfied thermal comfort. © 2010-2012 IEEE.

关键词： Deep reinforcement learning

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Noncollocated Output Feedback control for the Semilinear Parabolic PDEs Under Input and Output Quantization

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IEEE Transactions on Automatic control 2025年第5期70卷 2934-2947页

作者： Zhao, Xuena Liu, Zhijie Zhang, Shuang Liu, Yu He, Wei University of Science and Technology Beijing Institute of Artificial Intelligence Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Beijing100083 China School of Automation Science and Engineering Engineering Research Center of Precision Electronic Manufacturing Equipment of Ministry of Education Guangzhou510640 China Institute of Artificial Intelligence Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Beijing100083 China School of Automation Beijing102206 China

In the context of increasingly networked control systems, this article concentrates on the noncollocated control issue of input and output quantization for industrial processes governed by semilinear parabolic partial differential equation (PDE) systems. Both the control and measurement signals are quantized and transmitted through the network, which causes signal errors and reduces the stability and robustness of the system. A Luenberger-type PDE observer is developed to overcome the control difficulty of quantized measurement feedback signals and noncollocated pointwise sensors. Based on the observed value, a feedback controller is proposed, which undergoes quantization and operates on the distributed actuators within the domain. By employing the Lyapunov direct method, it is demonstrated that the observer can accurately track the system state with exponential precision, and the closed-loop system is exponentially converged. The well-posedness analysis of the closed-loop system is discussed via the C0-semigroup method. The performance of the developed control strategy is validated through simulation examples. © 1963-2012 IEEE.

关键词： Feedback control

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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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