Fixed-Time Incremental Neural Control for Manipulator Based on Composite Learning with Input Saturation

In this paper, an adaptive incremental neural network (INN) fixed-time tracking control scheme based on composite learning is investigated for robot systems under input saturation. Firstly, by integrating the method into INN to cope with inevitable dynamic uncertainty, a novel updating law of NN weights designed, which does not need satisfy stringent persistent excitation (PE) conditions. addition, saturated input, differing from adding auxiliary system, paper introduces hyperbolic tangent function deal saturation nonlinearity converting asymmetric constraints symmetric ones. Moreover, approach and Lyapunov theory are combined ensure that all signals closed-loop converge small neighborhood origin in fixed time. Finally, numerical simulation results verify effectiveness algorithm.