Model-free optimal decentralized sliding mode control for modular and reconfigurable robots based on adaptive dynamic programming

Bo Dong,Fan Zhou,Weibo Yu,Tianjiao An

doi:10.1177/1687814019896923

Bo Dong, Fan Zhou + Show 2 more

Open Access

https://doi.org/10.1177/1687814019896923

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Abstract

In this article, a model-free decentralized sliding mode control method is proposed based on adaptive dynamic programming algorithm to solve the problem of optimal trajectory tracking control of modular and reconfigurable robots. The dynamic model of modular and reconfigurable robot is formulated by a synthesis of joint subsystems with interconnected dynamic couplings. Based on sliding mode control technique, the optimal control problem of the modular and reconfigurable robot systems is transformed into an optimal compensation issue of unknown dynamics of each joint subsystems, in which the interconnected dynamic couplings effects among the subsystems are approximated by using the developed neural network identifier. Based on policy iteration scheme and the adaptive dynamic programming algorithm, the Hamilton–Jacobi–Bellman equation can be solved by using the critic neural network, so that optimal control policy can be obtained. The closed-loop system is proved to be asymptotically stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness of the method.

Highlights

Modular and reconfigurable robots (MRRs)[1] have attracted wide attention in robotics community since they are possessed of better structural adaptability and flexibility than conventional robots
As a useful tool to deal with disturbances, sliding mode control (SMC) technique may effectively improve the robustness of the nonlinear systems
Some researchers are presented by combining adaptive dynamic programming (ADP)-based optimal method with decentralized control scheme

Summary

Introduction

Modular and reconfigurable robots (MRRs)[1] have attracted wide attention in robotics community since they are possessed of better structural adaptability and flexibility than conventional robots. Tang et al.[21] proposed a learning-based adaptive optimal control method, which is used to solve the tracking problem of n-link robots. Some researchers are presented by combining adaptive dynamic programming (ADP)-based optimal method with decentralized control scheme.

Results

Conclusion