基于强化学习的扰动系统最优控制策略

Zhong Fan, Shihua Li, Rongjie Liu
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引用次数: 0

摘要

针对一类连续时间系统,提出了一种数据驱动的部分无模型最优控制策略。虽然一系列最优控制取得了优异的性能,但仍然存在以下挑战:(1)基于标称系统设计的控制器难以应对突发干扰。(ii)反馈控制高度依赖于系统动力学,通常需要完整的状态信息。针对这两种挑战,本文提出了一种结合输出反馈强化学习和输入输出干扰观测器的复合控制方法。首先,给出了一种输出反馈策略迭代算法,迭代获取反馈增益。同时,观测器不断地提供对扰动的估计。在我们的方法中,不需要预先知道系统动态信息和状态信息,从而提供了更高程度的鲁棒性和实际实现前景。最后,通过一个算例验证了所提控制器的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement Learning based Data-driven Optimal Control Strategy for Systems with Disturbance
This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a data-driven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. System dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.
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