Adaptive predictive control of three-tank-system

Abstract

This paper is focused in application of a self - tuning predictive controller for real - time control of a three - tank - system laboratory model. The objective laboratory model is a two input - two output (TITO) nonlinear system. It is based on experience with authentic industrial control applications. The controller integrates a predictive control synthesis based on a multivariable state - space model of the controlled system and an on - line identification of an ARX model corresponding to the state - space model. The model parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method. The optimization was realized by minimization of a quadratic objective function. Results of real-time experiments are also included.