Computational load reduction in model predictive control of nonlinear systems via decomposition

Abstract

The aim of this study is to reduce the computational load in model predictive control of multi-input nonlinear systems. First, the nonlinear system which has a high number of states and inputs is decomposed into several subsystems by solving a linear integer programming problem offline. Then, the model of each subsystem is revised by considering the effect of coupling and interactions of other subsystems. Next, the robust model predictive technique based on linear matrix inequalities is employed to compute control signal for each subsystem. An industrial chemical reaction example is used to illustrate the effectiveness of the proposed method.