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第36届中国控制会议

作者： Xian Yu Zhongsheng Hou Chenkun Yin Advanced Control systems Laboratory School of Electronics and Information EngineeringBeijing Jiaotong University

ISBN: (纸本)9781538629185

A novel data-driven iterative learning control（ILC） based on the adaptive tuning of the 2D learning gain,is proposed in this paper for a class of general discrete-time nonlinear SISO *** on an equivalent compact form dynamic linearization data model of the controlled nonlinear system in the iteration domain,an iterative learning law is formulated by using a recursive search algorithm to adaptively tune the 2D learning gain with only the requirement of the measured I/O data of the controlled nonlinear *** theoretical analysis shows that the proposed ILC guarantees convergence of the tracking *** effectiveness of the proposed ILC is validated by simulations on a complex unknown nonlinear system with time-varying structure,order and parameters.

关键词： Iterative Learning Control Compact Form Dynamic Linearization Recursive Search Algorithm discrete-time nonlinear system

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Variable structure control with sliding mode prediction for discrete-time nonlinear systems

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控制理论与应用（英文版） 2006年第2期4卷 140-146页

作者： Lingfei XIAO Hongye SU Xiaoyu ZHANG Jian CHU National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou Zhejiang 310027China

A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.

关键词： Variable structure control Sliding mode prediction discrete-time nonlinear system Pendulum experiment

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维普期刊数据库

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Distributed Cooperative Learning for discrete-time Strict-Feedback Multi Agent systems Over Directed Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1831-1844页

作者： Min Wang Haotian Shi Cong Wang IEEE the School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641 Peng Cheng Laboratory Shenzhen 518055China the School of Control Science and Engineering Shandong UniversityJinan 250061China the Center for Intelligent Medical Engineering Shandong UniversityJinan 250061China

This paper focuses on the distributed cooperative learning(DCL)problem for a class of discrete-time strict-feedback multi-agent systems under directed *** with the previous DCL works based on undirected graphs,two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric,and the derived weight error systems exist n-step *** novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying(LTV)systems with different phenomena including the nonsymmetric Laplacian matrix and time ***,an adaptive neural network(NN)control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating ***,by using two novel lemmas on the extended exponential convergence of LTV systems,estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and *** stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the“mod”function and proper time series.A simulation comparison is shown to demonstrate the validity of the proposed DCL method.

关键词： Cooperative learning control directed graphs discrete-time nonlinear system neural networks(NNs) strict-feedback systems

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维普期刊数据库

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