Prescribed Fixed-Time Adaptive Neural Control for Manipulators with Uncertain Dynamics and Actuator Failures

Abstract

In this paper, a fixed-time adaptive neural control scheme is proposed to solve the prescribed tracking problem of robot manipulators in the presence of uncertain dynamics, and stuck-type actuator failures which are unknown in time, pattern, and values. Technically, the combination of neural networks and adaptive control is used to handle the uncertainties in system dynamics, an adaptive compensation mechanism is designed to accommodate the failures occurring in actuators, and also a systematic design procedure based on the prescribed performance bounds is presented to establish the conditional inequality for ensuring fixed-time stability. With our scheme, it can be proved rigorously that the tracking errors in joint space can always be kept within the prescribed bounds, and converge to a small region of zero in a bounded settling time, in addition to the closed-loop signal boundedness. The proposed scheme is validated through simulations.