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Fixed-Time Anti-Disturbance Average-Tracking for Multi-Agent Systems Without Velocity Measurements

Journal of Systems Science & Complexity 2024年第3期37卷 985-1002页

作者： LI Yuling LIU Chenglin ZHANG Ya CHEN Yangyang Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuci 214122China School of Automation Southeast UniversityNanjing 210096China

In this paper,the authors tackle a fixed-time average-tracking problem for multi-agent systems which do not require velocity measurements and are affected by external mismatched and matched *** objective requires the considered agents with second-order dynamics to follow the average value of several time-varying reference signals,each of which is acquired by only one *** of all,the average value is estimated by using a novel distributed average estimator in a fixed ***,an original observer is created to provide estimated values in a fixed time for external mismatched and matched disturbances as well as unmeasured velocities of ***,the authors propose a novel fixed-time anti-disturbance average-tracking protocol with the aid of the observer and a sliding mode *** the end,the feasibility of theoretical analysis is illustrated by some simulation experiments.

关键词： Average-tracking control fixed-time algorithm fixed-time observer mismatched distur-bances multi-agent systems velocity measurements

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Multi-step state-based opacity for unambiguous weighted machines

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Science China(Information Sciences) 2024年第11期67卷 211-221页

作者： Zhipeng ZHANG Chengyi XIA Guoyuan QI Jun FU School of Artificial Intelligence Tiangong University School of Control Science and Engineering Tiangong University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Opacity is a central concept in the issue of privacy security and has been studied extensively in fields such as finite automata, probabilistic automata, and stochastic automata. Here, we investigate the problem of validating multi-step opaque properties through unambiguous weighted machines from the perspective of cyber-physical systems. First, the notion of multi-step state-based opacity for unambiguous weighted machines is presented and defined. It includes two variants of delays with a finite K and infinite steps. Subsequently, the weighted state estimate with K(infinite)-step delay is established by abstracting the possible state set that the system could have through these weighted observations. Meanwhile, to keep the observable weighted sequence consistent between the bidirectional observers, the unobserved weights of the reverse weighted machine are assumed to be reserved. Subsequently, the existence conditions are developed, and the corresponding algorithms, termed the weighted bidirectional observer, are generalized to verify these properties. Finally, several numerical examples are illustrated to demonstrate the effectiveness of the proposed method. Taken together, the current approach will be conducive to a deep understanding of the security and privacy of cyber-physical systems.

关键词： logical dynamical systems weighted state machine state estimation opacity cyber physical systems

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Improved High Order Model-Free Adaptive Iterative Learning control with Disturbance Compensation and Enhanced Convergence

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 343-355页

作者： Zhiguo Wang Fangqing Gao Fei Liu Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuxi214122China

In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization *** method adds the differential of tracking error in the criteria function to compensate for the effect of the random ***,a high-order estimation algorithmis used to estimate the value of pseudo partial derivative(PPD),that is,the current value of PPD is updated by that of previous *** the rapid convergence of the maximumtracking error is not limited by the initial value of *** convergence of the maximumtracking error is deduced in *** method can track the desired output with enhanced convergence and improved tracking *** examples are used to verify the convergence and effectiveness of the proposed method.

关键词： Pseudo partial derivative enhanced convergence tracking error disturbance compensation

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A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 709-718页

作者： Wei Xue Xiaoli Luan Shunyi Zhao Fei Liu IEEE the Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan University

In this paper,the Kalman filter(KF)and the unbiased finite impulse response(UFIR)filter are fused in the discrete-time state-space to improve robustness against *** avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics,we attempt to find a way to fuse without using noise *** fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response(IFIR)*** main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process,showing that a critical feature of the UFIR filter is *** numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed *** is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating ***,the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.

关键词： Fusion filter influence function Kalman filter(KF) robustness unbiased finite impulse response(FIR)

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Reinforcement learning for optimal tracking of large-scale systems with multitime scales

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Science China(Information Sciences) 2023年第7期66卷 5-29页

作者： Jinna LI Hao NIE Tianyou CHAI Frank L.LEWIS School of Information and Control Engineering Liaoning Petrochemical University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University UTA Research Institute University of Texas at Arlington

This paper aims to solve an optimal tracking control(OTC) problem of large-scale systems with multitime scales and coupled subsystems using singular perturbation(SP) theory and reinforcement learning(RL) techniques. A considerable contribution of this paper is the development of a data-driven SP-based RL method for the OTC of unknown large-scale systems with multitime scales. To achieve this, a multitime scale tracking problem was decomposed into a linear quadratic tracker problem for slow subsystems and a dynamical game problem for fast subsystems using the SP theory. Then, the distributed composite feedback controllers were found using a distributed off-policy integral RL algorithm that uses only measured data from the system in real time. Thus, the operational index can follow its prescribed target value via an approximately optimal approach. Theoretical analysis and proof are presented to demonstrate that the sum of the performances of reduced-order subsystems is approximately equal to the performance of the original large-scale system. Finally, numerical and practical examples are provided to validate the effectiveness of the proposed method.

关键词： reinforcement learning singular perturbation multitime scales data-driven control

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Self-triggered control for linear systems based on hierarchical reinforcement learning with RNNs 24

Self-triggered control for linear systems based on hierarchi...

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3rd International Conference on Computer, Artificial Intelligence and control Engineering, CAICE 2024

作者： Chen, Wei Wan, Haiying Luan, Xiaoli Liu, Fei Key Laboratory of Advanced Process Control for Light Industry Institute of Automation Jiangnan University Wuxi China

ISBN: (纸本)9798400716942

In this research, we introduce a novel model-free self-triggered control strategy for linear systems, employing a hierarchical reinforcement learning approach. This strategy establishes a dual-Actor Critic hierarchical architecture, utilizing two neural networks that guide each other's decision-making to simulate the triggering and control strategies in self-triggered control. The triggering strategy, serving as the top-level policy, aims to find the maximum triggering interval that maintains system stability based on the initial control strategy. The control strategy, as the bottom-level policy, under the guidance of the triggering strategy, takes the optimization of control performance considering the impact of triggering intervals as a sub-goal. This approach reduces the exploration space of the bottom-level policy and accelerates the reinforcement learning process. In the implementation of the dual-Actor Critic algorithm, the Critic network employs recurrent neural networks to capture the temporal dependencies of expected rewards, thereby enhancing the efficiency and accuracy in approximating the Q-function. The efficacy of our proposed method is demonstrated through a series of numerical simulations. Theory of computation∼Theory and algorithms for application domains∼Machine learning theory∼Reinforcement learning∼Sequential decision making. © 2024 ACM.

关键词： Federated learning

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Distributed Adaptive Cooperative control for Human-in-the-Loop Heterogeneous UAV-UGV Systems with Prescribed Performance

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Liang, Hongjing Yang, Shoufeng Li, Tieshan Zhang, Huaguang School of Automation Engineering University of Electronic Science and Technology of China Chengdu China College of Control Science and Engineering Bohai University Jinzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

This paper focuses on the distributed adaptive cooperative control problem for human-in-the-loop (HiTL) heterogeneous unmanned aerial vehicle-unmanned ground vehicle (UAV-UGV) systems via an improved prescribed performance approach. A novel human motion recognition system (HMRS) is designed and integrated into the HiTL strategy. Specifically, the leader trajectory can be changed in real time based on HMRS and the leader signal library to cope with various unexpected situations. This HiTL strategy solves the discontinuity and non-differentiability problems that may exist before and after human modification of leader signals within the extremely short time in conventional HiTL strategies. Moreover, an improved predefined-time prescribed performance approach is proposed, in which the performance function only needs to be first-order continuous differentiable instead of infinite-order continuous differentiable. This approach can greatly broaden the selection range of performance functions. Furthermore, a unified model of heterogeneous UAV-UGV systems is established to avoid designing UAV and UGV systems separately, which improves the universality of the control algorithm. Finally, the proposed HiTL control scheme is applied to a simulation example to verify its feasibility and effectiveness. IEEE

关键词： Intelligent vehicle highway systems

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Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 595-607页

作者： Jianguo Zhao Chunyu Yang Weinan Gao Linna Zhou Xiaomin Liu the Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationChina University of Mining and TechnologyXuzhou 221116 the School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116 IEEE the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation *** the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost *** the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement *** combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop *** proposed decomposition design not only bypasses the numerical stiffness but also alleviates the *** efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

关键词： Adaptive optimal control decentralized control output regulation reinforcement learning(RL) singularly perturbed systems(SPSs)

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Model-Free H2/H∞ Predictive control for Discrete-Time System via Q-Learning 13

Model-Free H2/H∞ Predictive Control for Discrete-Time Syste...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Lin, Yihong He, Peng Wan, Haiying Liu, Zhuangyu Luan, Xiaoli Liu, Fei Institute of Automation Jiangnan University Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Wuxi214122 China

ISBN: (纸本)9798350361674

This paper presents a model-free H2/H∞ Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm is employed to approximate the control inputs. Specifically, the developed algorithm is formulated in the form of linear matrix inequalities, designed to stabilize the system with mixed H2/H∞ control performance. Additionally, to improve robust performance under system disturbance variations, we introduce the receding horizon optimization into the Q-learning based predictive control. Finally, simulation results demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： H2/H∞ performance index Model-free predictive control Q-learning Receding horizon optimization Unknown discrete-time systems

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Graph-guided masked autoencoder for process anomaly detection

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process Safety and Environmental Protection 2024年 186卷 1345-1357页

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

Developing an accurate and reliable anomaly detection model is of great significance for safe operation in the process industry. To minimize false positives, it is crucial to accurately model the intricate topological and nonlinear connections among variables. In this study, a process anomaly detection method based on graph-guided masked autoencoder (GGMAE) is proposed by introducing the concept of the graph to the process industry. GGMAE first constructs a topology graph according to the process flowchart. Then, the patch and mask mechanism forces GGMAE to learn the process intrinsic information to reconstruct the input according to the variable topological relationship and temporal characteristics. Additionally, Kullback-Leibler divergence is used as the loss to ensure that the distribution of input and output is consistent. Experiments on two publicly available anomaly detection benchmarks demonstrate the superiority of GGMAE over existing methods. Visualization of the results demonstrates the physically compliant reconstruction logic. © 2024 The Institution of Chemical Engineers

关键词： Anomaly detection

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