Decentralized control for harmonic drive–based modular and reconfigurable robots with uncertain environment contact

Bo Dong,Yan Li,Keping Liu

doi:10.1177/1687814017696045

Abstract

In this article, a decentralized control strategy is presented for harmonic drive–based modular and reconfigurable robots with uncertain environment contact. Unlike conventional methods that rely on robot–environment contact model or force/torque sensing, this article addresses the problem of controlling modular and reconfigurable robots in contact with uncertain environment using only encoder data of each joint module. By employing a control-oriented harmonic drive model, the dynamic model of modular and reconfigurable robot is formulated as a synthesis of interconnected subsystems, in which the interconnected joint couplings are with small magnitudes. Based on the integral sliding mode control technique and the adaptive super-twisting algorithm, the decentralized controller is designed to compensate model uncertainty in which the up-bound is unknown. The stability of the modular and reconfigurable robot system is proved using Lyapunov theory. Finally, simulations are conducted for 2-degree-of-freedom modular and reconfigurable robots with different configurations under the situations of dynamic contact and collision to investigate the advantage of the proposed approach.

Highlights

Modular and reconfigurable robots (MRRs) comprise the robot modules, which contain power supplies, processing systems, actuators, and sensors
MRRs are always designed for satisfying various task requirements under uncertain environments; this means the dynamic model of MRRs will be changed under different configurations, and the contact model is difficult to be obtained
A stable decentralized control method is proposed for MRRs by employing adaptive fuzzy algorithm and sliding mode control technique for satisfying the conception of modular design;[18] building on these results, we proposed a reinforcement learning-based decentralized optimal control method[19] and a decentralized integral nested sliding mode control scheme for MRRs under the situation of time-varying constraint.[20]

Summary

Introduction

Modular and reconfigurable robots (MRRs) comprise the robot modules, which contain power supplies, processing systems, actuators, and sensors. In equation (15), tfi denotes the flexspline torque of harmonic drive including the environment constrained torque and the load torque; twi denotes the wave generator torque which is obtained using the motor torque command.

Results

Conclusion