基于数据的未知非仿射系统动态反馈近似最优控制

Jin-Jye Lin, Bo Zhao, Derong Liu
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引用次数: 0

摘要

本文提出了一种基于积分强化学习(IRL)的基于动态反馈的未知非仿射系统近似最优控制方法。对于非仿射系统的最优控制问题,由于输入增益矩阵是未知的,最优控制策略无法明确表示。因此,通过引入动态反馈信号作为虚拟控制输入,将非仿射系统转化为增广仿射系统。此外,通过为增广仿射系统设计合适的值函数,将增广仿射系统的最优控制表述为未知非仿射系统的AOC。此外,采用IRL方法通过纯临界结构推导出Hamilton-Jacobi-Bellman方程的近似解。理论分析表明,采用所提出的基于irl的AOC方案,闭环系统最终是均匀有界的。最后通过一个算例验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Based Approximate Optimal Control for Unknown Nonaffine Systems via Dynamic Feedback
In this paper, an integral reinforcement learning (IRL)-based approximate optimal control (AOC) method for unknown nonaffine systems is developed by using dynamic feedback. For optimal control problems of nonaffine systems, optimal control policy cannot be expressed explicitly since the input gain matrix is unknown. Therefore, the nonaffine system is transformed into an augmented affine system by introducing a dynamic feedback signal as the virtual control input. Moreover, by designing an appropriate value function for the augmented affine system, the optimal control of augmented affine system is formulated as the AOC for unknown nonaffine systems. Moreover, the IRL method is adopted to derive the approximate solution of Hamilton-Jacobi-Bellman equation via the critic-only structure. Theoretical analysis concludes that the closed-loop system is uniformly ultimately bounded by using the developed IRL-based AOC scheme. An example is utilized to demonstrate the effectiveness of the present approach.
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