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

Abstract

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