Title: Application of bounded error identification into model predictive control

Authors: Jian-Hong Wang

Addresses: School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, 343100, China

Abstract: In this paper, we apply bounded error identification into model predictive control (MPC). After introducing the family of models and some basic assumptions, we present bounded error identification to construct the interval predictor, using the neighbourhood of a given data point. To guarantee the obtained interval predictor to be a minimum interval predictor, two optimal vectors used to adjust the width of the obtained interval predictor are suggested to be piecewise affine forms, using the Karush-Kuhn-Tucker (KKT) optimality conditions. When to apply interval predictor into model predictive control, the midpoint of that interval is chosen in an optimisation problem to obtain the optimal control input for model predictive control. The property that control input only exists in its own cost function inspires us to use parallel distributed algorithms to solve unconstrained or constrained optimisation problem, and the detailed computational process on Newton algorithm and augmented Lagrangian algorithm are given for the sake of completeness. Finally the simulation example results confirm our theoretical results.

Keywords: bounded error identification; model predictive control; MPC; prediction; parallel distributed algorithm.

DOI: 10.1504/IJSSE.2018.093905

International Journal of System of Systems Engineering, 2018 Vol.8 No.3, pp.268 - 284

Received: 17 Oct 2017
Accepted: 27 Mar 2018

Published online: 19 Jul 2018 *

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