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

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.

Dynamic modeling and configuration adaptive control for modular reconfigurable robot

“…The robot's initial conditions are x(0) = ½0:5 0 0:25 0 T and x(0) = ½0:5 0 0:25 0 0:25 0 T and the initial values of the estimated parameters are u f (0) = u g (0) = 0. The control parameters are chosen as s 0 = 0:2, K 0 = 0:2I, e 0 = 0:2, e h = 0:1, h hi = 0:5, h 0 = 0:001, and l i = diag (25,25).…”

“…In recent years, the compensatory control method has attracted lots of researches and probably becomes the first choice. 25,26 Compared with the Jacobi method, the compensatory control method is to more precisely approximate the model uncertainty terms. For the MRR, the compensatory controller is designed not only for compensating uncertainties of one fixed-configuration model but also for adapting to the dynamics models with different configurations.…”