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A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems
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DOI：10.1109/JAS.2023.123186.png)
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摘要
En 中文
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.
Keyword:
Robust control
Uncertainty
Correlation
Neural networks
Reinforcement learning
Manipulators
Data processing
Data episodes
experience replay
neural networks
reinforcement learning (RL)
uncertain systems
期刊
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IF:
19.2
论文数:
1.4K
被引数:
1.1W
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