Adaptive Dynamic Programming-based Decentralized Sliding Mode Optimal Control for Modular and Reconfigurable Robots

被引：0

作者：

An, Tianjiao ^{[1
]}

Men, Xiaodong ^{[1
]}

Zhang, Xinyao ^{[2
]}

Li, Yuanchun ^{[1
]}

Dong, Bo ^{[1
]}

机构：

[1] Changchun Univ Technol, Dept Control Sci & Engn, Changchun, Peoples R China

[2] Northeastern Univ, Res Ctr Mech Kinet & Reliabil, Qinhuangdao, Hebei, Peoples R China

来源：

2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

Optimal control; Decentralized control; Adaptive dynamic programming; Modular and reconfigurable robot; MANIPULATOR;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A model-free decentralized sliding mode control (SMC) is proposed via adaptive dynamic programming (ADP) algorithm to solve the problem of optimal tracking control of modular and reconfigurable robots (MRRs) in this paper. The dynamic formulation of MRR is expressed by a synthesis of joint subsystems with interconnected dynamic couplings (IDCs). Based on SMC technique, the optimal control of robotic system is transformed into an optimal compensation problem of unknown dynamics of each subsystem and a neural network (NN) identifier is set up to approximate IDC dynamics. Based on ADP and policy iteration (PI) method, the Hamilton-Jacobi-Bellman (HJB) equation can be addressed by using the critic NN and the optimal control policy can be obtained. The closed-loop robotic system is proved to be asymptotic stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness of the method.

引用

页码：31 / 36

页数：6

共 14 条

[1] Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach [J].