Disturbance Observer-Based Fault-Tolerant Control for Robotic Systems With Guaranteed Prescribed Performance

The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actuator are considered and described, respectively, and a disturbance observer (DO) using neural networks is constructed to attenuate the influence of the unknown disturbance. Regarding the prescribed error bounds as time-varying constraints, the control design method based on barrier Lyapunov function (BLF) is used to strictly guarantee both the steady-state performance and the transient performance. A simulation study on a two-link planar manipulator verifies the effectiveness of the proposed controllers in dealing with the prescribed performance, the system uncertainties, and the unknown actuator failure simultaneously. Implementation on a Baxter robot gives an experimental verification of our controller.

Automated summary

This paper investigates the actuator failure compensation control problem for robotic systems with dynamic uncertainties. Control designs for partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actuator are considered, and a disturbance observer using neural networks is constructed to mitigate the influence of unknown disturbances. The control design method based on barrier Lyapunov function is utilized to ensure both steady-state and transient performance considering prescribed error bounds. Simulation and experimental studies demonstrate the effectiveness of the proposed controllers in dealing with prescribed performance, system uncertainties, and unknown actuator failures simultaneously.