Intelligent control toolkit for an advanced control system

This paper describes the development of a genetic algorithm based nonlinear controller. It builds on the successful integration of the modelling capability of an artificial neural network approach within the advanced control package Connoisseur^TM. The case for the long standing need for a generalised nonlinear controller for handling practical nonlinear and time varying systems is made. The motivation in terms of achieving tighter control, leading to increased efficiency and profitability are stressed. Existing techniques of gain scheduling and multiple models are briefly discussed, as well as their limitations. In the approach adopted in this paper the genetic algorithm based controller is employed to search for an optimal set of control outputs to minimise a given performance index. The paper gives examples of simulation studies and comments of the various factors that affect the performance of the controller and the practical implementation of the controller