title: Robust fault-tolerant control for a biped robot using a recurrent cerebellar model articulation controller.
abstract: A design technique of a recurrent cerebellar model articulation controller (RCMAC)-based fault-tolerant control (FTC) system is investigated to rectify the nonlinear faults of a biped robot. The proposed RCMAC-based FTC (RCFTC) scheme contains two components: 1) an online fault estimation module based on an RCMAC is used to provide approximation information for any nonnominal behavior due to the system failure and modeling error of the biped robot; and 2) a controller module consisting of a computed torque controller and a robust FTC is utilized to achieve FTC. In the controller module, the computed torque controller reveals a basic stabilizing controller to stabilize the system, and the robust FTC is utilized to compensate for the effects of the system failure so as to achieve fault accommodation. The adaptive laws of the RCFTC system are rigorously established based on the Lyapunov function, so that the stability of the system can be guaranteed. Finally, two simulation cases of a biped robot are presented to illustrate the effectiveness of the proposed design method. Simulation results show that the RCFTC system can effectively recover the control performance for the system in the presence of the nonlinear faults and modeling uncertainties.
authors.affiliation: Department of Electrical Engineering, Yuan Ze University, Jhongli City 32026, Taiwan, R.O.C. cml@saturn.yzu.edu.tw
authors.forename: Chih-Min
authors.initials: CM
authors.lastname: Lin
authors.forename: Chiu-Hsiung
authors.initials: CH
authors.lastname: Chen
mesh_terms: D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D002531:Cerebellum / Q000502:physiology*; D003198:Computer Simulation; D005246:Feedback; D008124:Locomotion / Q000502:physiology*; D008954:Models, Biological; D009038:Motion; D009415:Nerve Net / Q000502:physiology*; D012371:Robotics / Q000379:methods*