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

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