Gain scheduling adaptive model predictive controller for two conical tank interacting level system

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 gain scheduling 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 gain scheduling adaptive controller's output based on the current value of the measured process variable. The tuning parameters for the MPC controller are obtained using real coded Genetic Algorithm (GA). The capabilities of the gain scheduling adaptive (GSA) control strategy for MPC controller are investigated on Two Conical Tank Interacting Level System (TCTILS) through computer simulation.