Adaptive Prescribed Performance Control for Flexible-Joint Robotic Manipulators with Unknown Deadzone and Actuator Faults

Abstract

A prescribed performance neuro-adaptive control scheme is proposed for a single-link flexible-joint robotic manipulator with unknown deadzone and actuator faults. A new smooth deadzone inverse model is constructed to offset the adverse effect caused by the input deadzone in the actuator of flexible-joint manipulators. The control law is developed by coordinating prescribed performance control with a backstepping technique to ensure transient/steady-state performance, while adaptive neural networks are employed for uncertainty approximation. The tracking error is always restricted within the prescribed bound during the control process, and it ultimately converges to the small neighborhood of origin. All signals in the closed-loop flexible-joint robotic manipulator system are proved to be uniformly bounded. Simulation results are provided to demonstrate the efficiency of the prescribed performance adaptive neural network backstepping control scheme.