A Nonlinear Self-tuning Control Method Based on Neural Wiener Model

Abstract

In this work, a novel nonlinear self-tuning adaptive control scheme based on the neural Wiener model has been proposed to copy with a class of nonlinear uncertain systems. First the parameterization model with uncertain parameters is derived based on a linear transfer function model followed by neural networks. Then based on the performance index, the adaptive control strategy includes the system parameters identification and the control law calculation. Since the networks are linearly described by some basis functions, the closed-loop system stability can be ensured under some realistic assumptions. Finally, the proposed controller is applied to a pH control problem. The simulation results have demonstrated that the proposed nonlinear self-tuning control method is applicable, especially for its reliable set-point tracking and adaptive abilities.