B-Spline Wavelet Neural-Network-Based Adaptive Control for Linear-Motor-Driven Systems via a Novel Gradient Descent Algorithm

Abstract

This paper proposes a gradient descent (GD) algorithm-based B-Spline wavelet neural network (GDBSWNN) learning adaptive controller for linear motor (LM) systems under system uncertainties and actuator saturation constraints. A recursive least squares (RLS) algorithm-based indirect adaptive strategy is used to effectively estimate model parameters, which can guarantee they converge to true values. A novel GDBSWNN compensator is proposed to estimate the remaining complex uncertainties, where weights are updated by online GD training. An auxiliary system is integrated into the control scheme to address the saturation problem, which guarantees stability and satisfactory control performance when saturation occurs. In addition, a stability analysis is presented to prove that all signals of the closed-loop system are bounded using the Lyapunov theory. Experiments have been conducted on an LM-driven motion platform, where different controllers have been tested, demonstrating the effectiveness and advantages of the proposed approach.