Disturbance observer based actor-critic learning control for uncertain nonlinear systems  

作　　者：Xianglong LIANG Zhikai YAO Yaowen GE Jianyong YAO 

机构地区：[1]School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China [2]College of Artificial Intelligence,Nanjing University of Post and Telecommunication,Nanjing 210023,China

出　　处：《Chinese Journal of Aeronautics》2023年第11期271-280,共10页中国航空学报（英文版）

基　　金：supported by the National Key R&D Program of China(No.2021YFB2011300);the National Natural Science Foundation of China(No.52075262).

摘　　要：This paper investigates the disturbance observer based actor-critic learning control for a class of uncertain nonlinear systems in the presence of unmodeled dynamics and time-varying disturbances.The proposed control algorithm integrates a filter-based design method with actor-critic learning architecture and disturbance observer to circumvent the unmodeled dynamic and the timevarying disturbance.To be specific,the actor network is employed to estimate the unknown system dynamic,the critic network is developed to evaluate the control performance,and the disturbance observer is leveraged to provide efficient estimation of the compounded disturbance which includes the time-varying disturbance and the actor-critic network approximation error.Consequently,highgain feedback is avoided and the improved tracking performance can be expected.Moreover,a composite weight adaptation law for actor network is constructed by utilizing two types of signals,the cost function and the modeling error.Eventually,theoretical analysis demonstrates that the developed controller can guarantee bounded stability.Extensive simulations and experiments on a robot manipulator are implemented to validate the performance of the resulted control strategy.

关 键 词：Actor-critic structure Composite adaptation Disturbance observer Robot manipulator Uncertain nonlinear system 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]