Nonlinear model predictive control based on piecewise linear Hammerstein models

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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.

Keywords

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

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of System Engineering and Engineering ManagementCity University of Hong KongKowloon TongHong Kong
  2. 2.Institute of Control and Computation Engineering, Faculty of Electronics and Information TechnologyWarsaw University of TechnologyWarsawPoland

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