A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems |
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| C. X. Mu, Y. Zhang, G. B. Cai, R. J. Liu, and C. Y. Sun, “A data-based feedback relearning algorithm for uncertain nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1288–1303, May 2023. doi: 10.1109/JAS.2023.123186 |
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| Authors: | Chaoxu Mu Yong Zhang Guangbin Cai Ruijun Liu Changyin Sun |
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| Affiliation: | 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;2. College of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China;3. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;4. School of Automation, Southeast University, Nanjing 210096, China |
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| Abstract: | In this paper, a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear systems. Motivated by the classical on-policy and off-policy algorithms of reinforcement learning, the online feedback relearning (FR) algorithm is developed where the collected data includes the influence of disturbance signals. The FR algorithm has better adaptability to environmental changes (such as the control channel disturbances) compared with the off-policy algorithm, and has higher computational efficiency and better convergence performance compared with the on-policy algorithm. Data processing based on experience replay technology is used for great data efficiency and convergence stability. Simulation experiments are presented to illustrate convergence stability, optimality and algorithmic performance of FR algorithm by comparison. |
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| Keywords: | Data episodes experience replay neural networks reinforcement learning (RL) uncertain systems |
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