Fast and low‐frequency adaptation in neural network control

Abstract

In adaptive neural network (NN) control, fast adaptation through high‐gain learning rates can cause high‐frequency oscillations in control response resulting in system instability. This study presents a simple adaptive NN with proportional derivative (PD) control strategy to achieve fast and low‐frequency adaptation for a class of uncertain non‐linear systems. Variable‐gain PD control without the knowledge of plant bounds is proposed to semi‐globally stabilise the plant, so that NN approximation is applicable. A low‐pass filter‐based modification is applied to the adaptive law to filter out high‐frequency content, so that tracking performance can be safely improved by the increase of learning rates. The novelties of this study with respect to adaptive NN control are as follows: (i) semi‐global practical asymptotic tracking can be achieved by a simple adjustment of control parameters; and (ii) fast and low‐frequency adaptation can be obtained via high‐gain learning rates under guaranteed system stability. Simulation studies have demonstrated that the proposed approach can outperform its non‐filtering counterpart in terms of tracking accuracy, energy cost and control smoothness.