Structured Singular Valued based robust nonlinear model predictive controller using Volterra series models

Abstract

A methodology is proposed for designing a robust nonlinear model predictive controller based on a Volterra series model with uncertain coefficients. A key benefit of using the Volterra series model is that it can be split into a nominal and an uncertainty model thus permitting the application of robust analysis tools. The controller is based on the on-line solution of a robust performance test based on a Structured Singular Value norm. The cost function of the controller can be formulated to account for manipulated variable movement weighting, manipulated variable constraints and a terminal condition. Finally, the proposed methodology is applied to a single-input–single-output continuous stirred tank reactor problem and to a multiple-input–multiple-output pH neutralization process.