Robust Fault-Tolerant Control for Robotic System Using Recurrent Cerebellar Model Articulation Controller

摘要

In this paper, a design technique of recurrent cerebellar model articulation controller (RCMAC)-based fault-tolerant control (FTC) system against the fault of the robotic system is investigated. The proposed RCMAC-based FTC (RCFTC) scheme has two main components: (1) the online fault estimation module consisting of a nonlinear estimation model based on an RCMAC is used to provide the approximation information for any non-nominal behavior due to faults in the robotic system; and (2) the controller module consists of a computed torque controller and a fault-tolerant controller. In this controller module, the computed torque controller reveals a basic stabilizing controller to stabilize the system; then, relying on the approximation information of the fault, a fault-tolerant controller can be constructed to compensate for the effects of the fault of the system so as to achieve the fault accommodation. The adaptive laws of RCMAC are rigorously established based on the Lyapunov stability theory so that the stability of the RCFTC system can be guaranteed. Finally, the effectiveness of the proposed RCFTC scheme has been shown by a two-link robotic system. The simulation results show the effectiveness of the RCMAC-based fault-tolerant control for the system in the presence of system uncertainty and nonlinear fault.