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Output Regulation Based on Zero-Sum Game for Discrete-Time System driven by Exogenous Signal

ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年

作者： Song, Ruizhuo Yang, Gaofu Lewis, Frank L. Univ Sci & Technol Beijing Beijing Engn Res Ctr Ind Spectrum Imaging Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Texas Arlington UTA Res Inst Ft Worth TX 76118 USA

This article proposes a novel Q-learning algorithm that relies solely on input-output data to address the output regulation control problem of complex discrete-time systems affected by exogenous signals. Unlike traditional methods, this algorithm does not require detailed system information, state knowledge, or data about external systems or exogenous signals. Additionally, the control strategy does not depend on state information, but on input-output data processed by a set of filters. We provide upper and lower bounds on the discount factor, eliminating the need to solve the Riccati equation. These bounds ensure that the value function remains finite, and we prove the stability of the system when using control inputs derived from the value function with the given discount factor. Furthermore, the Q-learning algorithm, when applied with input data containing probing noise, is shown to yield Q-function estimates that are independent of the probing noise. Finally, a simulation involving a grid-connected inverter is presented, demonstrating the effectiveness of the proposed algorithm in a practical setting.

关键词： Regulation Q-learning Noise Optimal control Riccati equations Heuristic algorithms Game theory Output feedback Games Asymptotic stability Adaptive dynamic programming discrete-time systems game theory output feedback reinforcement learning

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Model Predictive control-Based Value Estimation for Efficient Reinforcement learning

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ieee INTELLIGENT systems 2024年第3期39卷 63-72页

作者： Wu, Qizhen Liu, Kexin Chen, Lei Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China

Reinforcement learning (RL) suffers from limitations in real practices primarily due to the number of required interactions with virtual environments. It results in a challenging problem because we are implausible to obtain a local optimal strategy with only a few attempts for many learning methods. Hereby, we design an improved RL method based on model predictive control that models the environment through a data-driven approach. Based on the learned environment model, it performs multistep prediction to estimate the value function and optimize the policy. The method demonstrates higher learning efficiency, faster convergent speed of strategies tending to the local optimal value, and less sample capacity space required by experience replay buffers. Experimental results, both in classic databases and in a dynamic obstacle-avoidance scenario for an unmanned aerial vehicle, validate the proposed approaches.

关键词： Predictive models Neural networks Trajectory data models Computational modeling Training Optimization Reinforcement learning Predictive control

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data-driven Robust Iterative learning Predictive control for MIMO Nonaffine Nonlinear systems With Actuator Constraints

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9850-9860页

作者： Zhang, Chong Hu, Yunfeng Xiao, Lin Gong, Xun Chen, Hong Jilin Univ Natl Key Lab Automot Chassis Integrat & Bion & Con Changchun 130025 Peoples R China Jilin Univ Coll Commun Engn Changchun 130025 Peoples R China Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China Hunan Normal Univ MOE LCSM Changsha 410081 Peoples R China Jilin Univ Sch Artificial Intelligence Changchun 130012 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China

The coupling of multivariate repeated systems and the nonlinearity that is difficult to characterize through mechanisms, along with actuator constraints and data noise pollution, pose challenges in achieving precise tracking tasks. To address these issues, a novel data-driven robust iterative learning predictive control (ILPC) scheme is proposed. The contribution lies in its ability to achieve multivariable tracking without requiring any prior model information, all while effectively suppressing noise pollution and actively addressing actuator constraints. Specifically, a dynamic linearization data predictive model (DLDPM) is first obtained for system dynamic behavior prediction and controller synthesis. The estimation of the unknown pseudoJacobian matrix (PJM) in DLDPM was previously overlooked in terms of data noise suppression mechanisms. In this study, we utilize a noise-tolerant zeroing neural network (NT-ZNN) for its estimation. Theoretical analysis confirms that the PJM adaptive estimation law can achieve residue-free convergence and its robustness in noise suppression. Then, a constrained ILPC scheme is proposed, which transforms the multivariable tracking problem with actuator constraints into an iteration-varying quadratic programming problem with both inequality and equality constraints, which is solved using NT-ZNN. Theoretical proofs substantiate that a constrained ILPC scheme can achieve asymptotic convergence along the iterative axis. Finally, the proposed scheme is validated in a thermal management system for a proton exchange membrane fuel cell, showcasing the effectiveness in tracking tasks and handling actuator constraints in the presence of noise pollution.

关键词： Convergence analysis data-driven iterative learning predictive control (ILPC) dynamic linearization zeroing neural network

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Fan flue gas temperature control system based on fuzzy PID control 12

Fan flue gas temperature control system based on fuzzy PID c...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Wang, Zihao Li, Xiaoli Wang, Kang Li, Yang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Beijing 100124 Peoples R China CUC Sch Int Studies Beijing 100024 Peoples R China

ISBN: (纸本)9798350321050

Due to the complex process of flue gas acid production and numerous processes, the controlled objects in the whole process have the characteristics of time-variation, randomness, nonlinearity and large lag. For SO2 fan, because the traditional PID control adopts the parameters set in advance, it is difficult to play an effective role in the complex flue gas acid production system, therefore, a fuzzy PID control method is proposed. The two-dimensional structure fuzzy controller model with two inputs and three outputs can dynamically adjust the PID controller parameters. By establishing a transfer function model for factory temperature data, we compare the fuzzy PID control with the traditional PID controller. The results demonstrate that the fuzzy PID controller possesses strong anti-interference ability, has a shorter adjustment time, and exhibits a smaller overshoot volume, which enables more effective control of the fan outlet temperature.

关键词： Outlet Temperature of Fan PID control Fuzzy PID control

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Distributed Meta-Deep Reinforcement learning for Wide Area Frequency control of Interconnected Grid Considering Multi-Time Scale Coordination

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ieee TRANSACTIONS ON CIRCUITS AND systems I-REGULAR PAPERS 2025年

作者： Li, Jiawen Dai, Jichao Shanghai Univ Elect Power Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

To solve the problems of over-shedding for loads in conventional frequency emergency control and prevent constraint violations in power systems, a data-driven wide-area frequency control strategy (WA-FCS) is proposed. The WA-FCS includes an emergency frequency control (EFC) module and a wide automatic generation control (WAGC) module. In online decisions, the EFC can consider the impact of WAGC on the system frequency and prevent system constraint violations. WAGC helps EFC quickly solve the problem of line power overload. By coordinating EFC with WAGC, the load loss following grid failure can be reduced. In addition, to improve the strategy's robustness, a distributed meta-actor critic (DMAC) algorithm is proposed, which draws on meta-learning and distributed exploration to obtain the optimal WA-FCS strategy in a random environment. The simulation results of this paper verify the effectiveness and feasibility of the proposed strategy for the performance-based frequency regulation market.

关键词： Frequency control Frequency measurement Automatic generation control Accuracy Reliability Regulation Real-time systems Accidents Time-frequency analysis Power grids emergency frequency control performance-based frequency regulation market automatic generation control distributed meta-actor critic

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A data-driven Bayesian Koopman learning Method for Modeling Hysteresis Dynamics

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2024年第11期35卷 15615-15623页

作者： Huang, Xiang Zhang, Hai-Tao Wang, Jun Huazhong Univ Sci & Technol Engn Res Ctr Autonomous Intelligent Unmanned Syst Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Exploring the mechanism of hysteresis dynamics may facilitate the analysis and controller design to alleviate detrimental effects. Conventional models, such as the Bouc-Wen and Preisach models consist of complicated nonlinear structures, limiting the applications of hysteresis systems for high-speed and high-precision positioning, detection, execution, and other operations. In this article, a Bayesian Koopman (B-Koopman) learning algorithm is therefore developed to characterize hysteresis dynamics. Essentially, the proposed scheme establishes a simplified linear representation with time delay for hysteresis dynamics, where the properties of the original nonlinear system are preserved. Furthermore, model parameters are optimized via sparse Bayesian learning together with an iterative strategy, which simplifies the identification procedure and reduces modeling errors. Extensive experimental results on piezoelectric positioning are elaborated to substantiate the effectiveness and superiority of the proposed B-Koopman algorithm for learning hysteresis dynamics.

关键词： Hysteresis Heuristic algorithms Nonlinear dynamical systems Bayes methods Power system dynamics learning systems Brain modeling Hysteresis Koopman operator modeling piezoelectric actuators (PEAs)

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Multi-agent Adaptive Virtual Inertia Cooperative control of AC/DC Micro-grid Based on Virtual Synchronous Generators 12

Multi-agent Adaptive Virtual Inertia Cooperative Control of ...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Dou, Zhenlan Tang, Lianqing Zhang, Chunyan Yang, Haitao Han, Dong Jiang, Jingjing Xu, Dezhi State Grid Shanghai Municipal Elect Power Co Shanghai 200023 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China State Grid Shanghai Shibei Elect Power Supply Co Shanghai 200072 Peoples R China

ISBN: (纸本)9798350321050

driven by new energy technology, the scale of AC/DC micro-grid connected to the power grid is growing. In order to make distributed power source have similar inertia support and damping characteristics as synchronous generator (SG), virtual synchronous generator (VSG) technology is introduced into inverter control. To solve the problems of frequency consistency and oscillation in the parallel operation of multiple VSGs, a multi-agent adaptive virtual inertia cooperative control strategy based on VSGs is proposed. Firstly, an advanced control method based on phase-locked loop is introduced to suppress the impact of frequency and power caused by grid connection. Then, a multi-agent adaptive virtual inertia cooperative control strategy is proposed to shorten the adjustment time required for the system to reach stability and improve the transient performance of the system. Finally, the simulation experiment verifies that the proposed control strategy can make the frequency of the parallel VSGs reach the same quickly, weaken the oscillation phenomenon in the frequency regulation process, and improve the transient performance of the system.

关键词： Micro-grid Virtual Synchronous Generator Consensus Algorithm Seamless Switching Adaptive Algorithm

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A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Sliding Mode control 13

A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Slid...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhou, Guoliang Wu, Lingwei School of Intelligent Manufacture Lishui Vocational and Technical College Lishui323000 China School of Intelligent Manufacture Taizhou University Taizhou318000 China

ISBN: (纸本)9798350361674

In this paper, a hyperbolic-secant reaching law-based sliding mode control method is presented for uncertain discrete-time controlled systems. The hyperbolic-secant reaching law based on the hyperbolic-secant function and the saturation function is designed to realize the increasing of the reaching speed and the reduction of the chattering. To characterize the tracking performance of the discrete controlled system, the detailed results about the absolute attractive layer bound and the quasi-sliding mode band are given and analyzed. The validity and usefulness of the proposed control approach can be verified by numerical simulation results. © 2024 ieee.

关键词： Sliding mode control

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Cooperative Fault-Tolerant control for a Class of Nonlinear MASs by Resilient learning Approach

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2024年第1期35卷 670-679页

作者： Deng, Chao Yue, Dong Che, Wei-Wei Xie, Xiangpeng Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China

In this article, a learning-based resilient fault-tolerant control method is proposed for a class of uncertain nonlinear multiagent systems (MASs) to enhance the security and reliability against denial-of-service (DoS) attacks and actuator faults. With the framework of cooperative output regulation, the developed algorithm consists of designing a distributed resilient observer and a decentralized fault-tolerant controller. Specifically, by using the data-driven method, an online resilient learning algorithm is first presented to learn the unknown exosystem matrix in the presence of DoS attacks. Then, a distributed resilient observer is proposed working against DoS attacks. In addition, based on the developed observer, a decentralized adaptive fault-tolerant controller is designed to compensate for actuator faults. Moreover, the convergence of error systems is shown by using the Lyapunov stability theory. The effectiveness of our result is examined by a simulation example.

关键词： Fault tolerant systems Fault tolerance Actuators Denial-of-service attack Observers Security Regulation Actuator faults adaptive control distributed learning control fault-tolerant control

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Dynamic Encoding-Decoding-Based Quantized Iterative learning control 13

Dynamic Encoding-Decoding-Based Quantized Iterative Learning...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Huo, Niu Shen, Dong School of Mathematics Renmin University of China Beijing100872 China

ISBN: (纸本)9798350361674

The tracking performance of linear discrete-time systems under quantized iterative learning control is investigated in this paper. An encoding-decoding mechanism is utilized to process the output of the system that is transmitted over the network channel. The encoding method includes the quantization procedure, which employs a uniform quantizer with finite quantization levels. The scaling parameters in the encoder and decoder are formulated to dynamically adjust based on the input of the quantizer. The learning control scheme is designed based on the output generated by the decoder. The results demonstrate that the system attains error-free tracking performance. The theoretical findings of this research are confirmed by a numerical simulation. © 2024 ieee.

关键词： Encoding (symbols)

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