Nonlinear model predictive control based on piecewise linear Hammerstein models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1001-1021
Journal / PublicationNonlinear Dynamics
Volume92
Issue number3
Online published15 Feb 2018
Publication statusPublished - May 2018

Abstract

This paper develops a nonlinear model predictive control (MPC) algorithm for dynamic systems represented by piecewise linear (PWL) Hammerstein models. At each sampling instant, the predicted output trajectory is linearized online at an assumed input trajectory such that the control actions can be easily calculated by solving a quadratic programming optimization problem, and such linearization and optimization may be repeated a few times for good linear approximation accuracy. A three-step procedure is developed to linearize a PWL function, where the derivatives of a PWL function are obtained by a computationally efficient look-up table approach. Unlike many existing MPC algorithms for Hammerstein systems, it does not require the inversion of static nonlinearity and can directly cope with input constraints even in multivariable systems. Two benchmark chemical reactors are studied to illustrate the effectiveness of the proposed algorithm.

Research Area(s)

  • Chemical reactor, Hammerstein model, Multistep linearization, Nonlinear model predictive control, Piecewise linear function

Citation Format(s)

Nonlinear model predictive control based on piecewise linear Hammerstein models. / Zhang, Jian; Chin, Kwai-Sang; Ławryńczuk, Maciej.

In: Nonlinear Dynamics, Vol. 92, No. 3, 05.2018, p. 1001-1021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review