Multiple Model Predictive Control of Nonlinear Systems Based on the Weighting Controller

Abstract

Model predictive control (MPC) is a mature technology and has become the standard approach for implementing constrained, multivariable control in the process industries today. The objective of this work is to introduce a multiple model predictive control (MMPC) strategy for multivariable nonlinear systems. For nonlinear systems in the actual industrial process, we need to linearize the nonlinear system at a number of operating point to get multiple local linear models to approximate the nonlinear system. Based on each local linear model of the set, a local linear MPC controller is designed. Through the recursive Bayesian weighting strategy, the weightings of the linear MPC controller outputs are then calculated to obtain the actual control variables of the system. Finally, this strategy is applied to Two Tank Conical Interacting System (TTCIS). Simulation results with TTCIS are provided to demonstrate the effectiveness and practicality of the proposed strategy.