A Multiple Model Adaptive Control Strategy for Model Predictive Controller for Interacting Non Linear Systems

Abstract

Model predictive control (MPC) has become the leading form of advanced multivariable control in the chemical process industry. The objective of this work is to introduce a multiple model adaptive control strategy for multivariable MPC. The method of approach is to design multiple linear MPC controllers. This strategy maintains performance of multiple linear MPC controllers over a wide range of operating levels. One important contribution is that the strategy combines several multiple linear MPC controllers, each with their own linear state space model describing process dynamics at a specific level of operation. One of the linear MPC controller output is selected as multiple model adaptive controller's output based on the current value of the measured process variable. The tuning parameters for the linear MPC controller are obtained using Genetic Algorithm (GA). The capabilities of the multiple model adaptive strategy for MPC controller are investigated on Two Tank Conical Interacting System (TTCIS) through computer simulation.