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Cooperative Output Regulation of Heterogeneous systems over Directed Graphs:A Dynamic Adaptive Event-Triggered Strategy

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Journal of systems Science & complexity 2023年第3期36卷 909-921页

作者： SU Hang CHENG Bin LI Zhongkui State Key Laboratory for Turbulence and Complex Systems and Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBeijing 100871China Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 201804China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210China

This paper investigates the cooperative output regulation problem of heterogeneous linear multi-agent systems over directed graphs with the constraint of communication *** that there exists an exosystem whose state information is not available to all agents,the authors develop distributed adaptive event-triggered observers for the followers based on relative information between neighboring *** should be pointed out that,two kinds of time-varying gains are introduced to avoid relying on any global information associated with the network,and dynamic triggering conditions are designed to get rid of continuous *** the basis of the designed observers,the authors devise a local controller for each *** with the existing related works,the main contribution of the current paper is that the cooperative output regulation problem for general directed graphs is solved requiring neither global information nor continuous communications.

关键词： Adaptive control consensus cooperative output regulation directed graph event-triggered control multi-agent systems

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Pt-Containing Ag_2S-Noble Metal Nanocomposites as Highly Active Electrocatalysts for the Oxidation of Formic Acid

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Nano-Micro Letters 2014年第3期6卷 252-257页

作者： Hui Liu Yan Feng Hongbin Cao Jun Yang State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Research Centre for Process Pollution Control Institute of Process Engineering Chinese Academy of Sciences

Nanocomposites with synergistic effect are of great interest for their enhanced properties in a given application. Herein, we reported the high catalytic activity of Pt-containing Ag2S-noble metal nanocomposites in formic acid oxidation, which is a key reaction in direct formic acid fuel cell. The electrochemical measurements including voltammograms and chronoamperograms are used to characterize the catalytic property of Pt-containing nanocomposites for the oxidation of formic acid. In view of the limited literatures on using nanocomposites consisting of semiconductor and noble metals for catalyzing the reactions of polymer electrolyte membrane-based fuel cells, this study provides a helpful exploration for expanding the application of semiconductor-noble metal nanocomposites.

关键词： Nanocomposites Synergistic effect Formic acid oxidation Direct formic acid fuel cell

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Self-supervised Depth Completion with Adaptive Online Adaptation 23

Self-supervised Depth Completion with Adaptive Online Adapta...

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7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023

作者： Chen, Yang Tan, Yang Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450398633

Although depth completion has achieved remarkable performance relying on deep learning in recent years, these models tend to suffer a performance degradation when exposed to new environments. Online adaptation, where the model is trained in a self-supervised manner during testing, seems a promising technique to alleviate the drop. However, continuous online adaptation may cause the model to over-adapt and miss the optimal parameters, resulting in oscillation or even degradation of the model performance, in addition to wasting computational resources. Therefore, this paper proposes an adaptive online adaptation framework to make model adaptively trigger online adaptation when encountering novel environments and stop adaptation when model has adapted to the current environment. In detail, we design a trigger to detect the familiarity of model to the current scenario based on image similarity and then launch online adaptation when the scenario is novel. Besides, we elaborate a stopper to monitor the error between prediction and depth input and convert online adaptation to inference when online adaptation does not bring improvement for model. Experimental results demonstrate that our method improves the accuracy of model prediction and increases average running speed of the model on each frame in online adaptation. © 2023 ACM.

关键词： Deep learning

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Inverse Reinforcement Learning Integrated Reinforcement Learning for Single Intersection Traffic Signal control 5

Inverse Reinforcement Learning Integrated Reinforcement Lear...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Gu, Shiyi Zhang, Tingting Zhang, Ya Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China Army Engineering University College of Command and Control Engineering Nanjing210017 China

ISBN: (纸本)9798350325294

Traffic Signal control (TSC) optimization is vital for efficient traffic flow in urban intersections. Recent research has explored the application of Reinforcement Learning (RL) agents to tackle TSC challenges. However, the selection of an appropriate reward function in RL agent training still relies on subjective judgment and experience. To address this issue, an Inverse Reinforcement Learning (IRL) integrated RL algorithm is proposed. In the IRL learning section, expert trajectory data are collected and analyzed by Relative Entropy IRL (REIRL). The latent reward of expert policy is reconstructed and utilized in RL agent training process. In the RL control section, a Double Dueling Deep Q Network is applied under a cycle control framework. As verified in simulations, the introduction of expert experience improves the performance of the RL agent to the expert level and concurrently enables robustness to expert policy noises. © 2023 IEEE.

关键词： Reinforcement learning

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Nonlinear robust control design for SSSC to improve damping oscillations and transient stability of power system 36

Nonlinear robust control design for SSSC to improve damping ...

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第36届中国控制会议

作者： Bangjun Lei Xiang Wu Shumin Fei Key Laboratory of power big data of Guizhou Province College of Electrical and Information EngineeringGuizhou Institute of Technology School of Mathematical Sciences Guizhou Normal University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast University

ISBN: (纸本)9781538629185

In this article, a nonlinear robust（NR） control is designed for static synchronous series compensator（SSSC） to improve transient stability and oscillation damping of power system. By taking the parameter deviations, external disturbances and unmodeled dynamics into account, the nonlinear dynamic model of SSSC can be changed into a Hamiltonian structure with the help of coordinate transformation. Based on the Hamiltonian structure of SSSC, a nonlinear robust control is designed for SSSC. Numerical simulation results on the two-area four-machine power system demonstrate the robustness and effectiveness of the proposed nonlinear robust control in comparison with the direct feedback linearization（FL） control. The study results show that the nonlinear robust control has better performance than the FL control.

关键词： SSSC nonlinear robust control Hamiltonian function method oscillations damping transient stability

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A Novel Forwarding Method for Full-Duplex Two-Way Relay Networks

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China Communications 2017年第4期14卷 65-72页

作者： Shuangshuang Han Xiang Cheng Liuqing Yang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing China and Qingdao Academy of Intelligent Industries The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Department of Electrical and Computer Engineering Colorado State University

An estimate-and-forward(EF) scheme for single-input single-output(SISO) and multiple-input multiple-output(MIMO) full-duplex two-way relay networks is proposed and analyzed. The relay estimates the received signal from two terminal nodes by a minimum mean squared error(MMSE) estimation and forwards a scaled version of the MMSE estimate to the destination. The proposed EF outperforms conventional amplify-and-forward(AF) and decode-and-forward(DF) across all signal-to-noise ratio(SNR) region. Because its computational complexity is high for relays with a large number of antennas(large MIMO) and/or high order constellations, an approximate EF scheme, called list EF, are thus proposed to reduce the computational complexity. The proposed list EF computes a candidate list for the MMSE estimate by using a sphere decoder, and it approaches the performance of the exact EF relay at a negligible performance loss. The proposed forwarding approach also could be used to other relay networks, such as half-duplex, one-way or massive MIMO relay networks.

关键词： estimate-and-forward full-duplex two-way relay wireless communications

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Robust Decentralized control of Interconnected Nonlinear systems Subject to Uncertainties and Disturbances via Generalized Extended State Observers 34

Robust Decentralized Control of Interconnected Nonlinear Sys...

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第三十四届中国控制会议

作者： ZHANG Chuanlin YANG ning HE Shuaipeng JIA Ruting Shanghai Engineering Research Center of Intelligent Management and Control for Power Process College of Automation EngineeringShanghai University of Electric Power Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Electrical and Computer Engineering Department California State University

ISBN: (纸本)9789881563897

This paper studies the problem of robust decentralized control for a class of large-scale interconnected systems subject to uncertainties and disturbances via a generalized active disturbance rejection control method. First, a novel extended state observer(ESO) is constructed separately for each subsystem with less dependence of the precise system information and structure. Second, an explicit formula of the decentralized active disturbance rejection control(DADRC) law is presented by utilizing the output feedback domination approach. It is shown that by a delicate analysis procedure, the closed-loop system can be rendered semi-globally asymptotically stable only using decentralized output feedback anti-disturbance controller. Numerical simulations show the efficiency of the proposed method.

关键词： output feedback control global stabilization disturbance rejection control extended state observer

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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Optimal Tracking control for Reconfigurable Manipulators Based on Critic-only Policy Iteration Algorithm 36

Optimal Tracking Control for Reconfigurable Manipulators Bas...

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第36届中国控制会议

作者： Hongbing Xia Bo Zhao Yuanchun Li Department of Control Science and Engineering Changchun University of Technology The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper tackles the optimal tracking control problem for reconfigurable manipulators based on critic-only policy iteration（Co PI） algorithm. By system transformation, the optimal tracking control problem is transformed into an optimal regulation problem. The optimal tracking controller is composed of the desired controller and the approximate optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, while the approximate optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. Then, a critic neural network is used to estimate the optimal performance index function, and the optimal feedback control is obtained by the Co PI algorithm. The convergence of the proposed method is analyzed and it is shown that the closed-loop system based on Co PI is uniformly ultimately bounded by using the Lyapunov approach. Finally, simulation studies are given to show the effectiveness of the developed method.

关键词： Adaptive dynamic programming Reconfigurable manipulators Optimal tracking control Policy iteration Neural networks

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Decentralized optimal control for modular and reconfigurable robots based on adaptive dynamic programming 36

Decentralized optimal control for modular and reconfigurable...

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第36届中国控制会议

作者： Zixu Wang Bo Dong Hongbing Xia Yuanchun Li Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a decentralized optimal control method for modular and reconflgurable robots（MRRs） based on adaptive dynamic ***,the dynamic model of MRRs is formulated by using the Newton-Euler iterative algorithm,and then the state space description is ***,the optimal control policy of the MRRs system is obtained based on the policy iteration algorithm,which is used to solve the Hamilton-Jacobi-Bellman（HJB） equation via the critic neural ***,the stability of the closed-loop system is proved by using the Lyapunov ***,simulations are conducted to illustrate the effectiveness for the 2-DOF MRRs.

关键词： Modular and reconfigurable robot Decentralized control strategy Adaptive dynamic programming Neural network

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