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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Wang, Ding Ha, Mingming 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 Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, an effective mixed driven framework is constructed involving both data and event considerations. The primary purpose lies in that the mixed driven iterative adaptive critic method is established to address approximate optimal control towards discrete-time nonlinear dynamics. The neural dynamic programming technique is inventively integrated with the mixed driven architecture, such that the knowledge of the controlled plant is needless and the number for updating control inputs is prominently reduced. A triggering threshold is also designed with theoretical guarantee, which renders that the control signals can be updated conditionally. Through carrying out simulation studies with comparisons, the superiority of the present near-optimal regulation approach is confirmed at last. Copyright (C) 2020 The Authors.

关键词： Discrete-time nonlinear dynamics iterative adaptive critic algorithm mixed driven design neural optimal control triggering condition

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Mixed Driven iterative adaptive critic Control Design Towards Nonaffine Discrete-Time Plants ⁎

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IFAC-PapersOnLine 2020年第2期53卷 3803-3808页

作者： Ding Wang Mingming Ha Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing 100124 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China

关键词： Discrete-time nonlinear dynamics iterative adaptive critic algorithm mixed driven design neural optimal control triggering condition

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