A new neural network and pole placement based adaptive composite controller

Abstract

The paper describes a new composite control method combining a neural network estimator with a conventional pole-placement based adaptive controller. The neural network estimation technique presented by Hussain (2000) is particularly effective when there is no complete plant information, or when considering a controlled plant as a 'black box'. In the proposed composite controller, the neural network estimator weights are adapted online to minimise the identification error, and these weights are fed into a robust self-tuning PID controller which provides an adaptive mechanism to ensure that the closed loop poles are placed at the desired positions. Simulation results show that the proposed method applies to general linear or nonlinear control systems.