Model based Controller Design using Real Time Neural Network Model and PSO for Conical Tank System

Abstract

Most of the industrial processes are nonlinear in nature, demanding an optimal control structure. Conventional controllers do not handle the nonlinear system behaviour effectively and they also have tuning associated problems. In this study, a simplified new generation RTDA (Robustness, Set point tracking, Disturbance Rejection, Aggressiveness) controller is designed for a nonlinear conical tank system. The enhanced features of RTDA controller enables us to tune the parameters separately without affecting each other to obtain optimum performance. The proposed study uses NARX (Nonlinear Autoregressive with Exogenous input) neural model for the RTDA controller design as it offers prior multi-step ahead predictions to predict the future plant behaviour. The performance of NN (Neural Network) based RTDA controller was proved to be better through simulation using MATLAB/Simulink. It requires multiple trials to determine the optimal or near optimal values for the tuning parameters for the NN based RTDA controller design and hence a highly skilled meta-heuristic algorithm called Particle Swarm Optimization (PSO) is used to serve that purpose. Thus PSO based NN- RTDA controller serves as an optimal controller for controlling the conical tank process.