论文标题
基于学习的不确定非线性系统的基于学习的干扰拒绝控制
Reinforcement Learning-based Disturbance Rejection Control for Uncertain Nonlinear Systems
论文作者
论文摘要
本文研究了具有非简单名称模型的不确定的非线性系统的基于增强学习（RL）的扰动排斥控制。最初设计的扩展状态观察者（ESO）旨在估计系统状态和总不确定性，这代表了对标称系统动力学的扰动。根据观察者的输出，使用基于经验的RL技术的模拟，控制控制可以补偿实时的总不确定性，同时在线估算了补偿系统的最佳策略。进行了严格的理论分析，以表明系统状态与原点以及已开发的政策对理想最佳政策的实际融合。值得一提的是，在既定框架中不需要广泛使用的激发持续性（PE）条件。提出了仿真结果，以说明该方法的有效性。
This paper investigates the reinforcement learning (RL) based disturbance rejection control for uncertain nonlinear systems having non-simple nominal models. An extended state observer (ESO) is first designed to estimate the system state and the total uncertainty, which represents the perturbation to the nominal system dynamics. Based on the output of the observer, the control compensates for the total uncertainty in real time, and simultaneously, online approximates the optimal policy for the compensated system using a simulation of experience based RL technique. Rigorous theoretical analysis is given to show the practical convergence of the system state to the origin and the developed policy to the ideal optimal policy. It is worth mentioning that, the widely-used restrictive persistence of excitation (PE) condition is not required in the established framework. Simulation results are presented to illustrate the effectiveness of the proposed method.
