Title: Robust model predictive control based on interval predictor estimation

Authors: Wang Jianhong; Ricardo A. Ramirez-Mendoza; Meng He; Jorge De J. Lozoya Santos

Addresses: School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, 64849, Mexico ' School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, 64849, Mexico ' School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, 341000, China ' School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, 64849, Mexico

Abstract: When considering three uncertainties in one state space equation, the interval predictors are constructed for state estimation and prediction output respectively through our own algebraic computations. After applying this interval predictor of prediction output into robust model predictive control strategy, one min-max optimisation problem is solved to obtain the optimal control input. Max operation is used to embody that the prediction output is not a constant value, but one interval. Using the parabola property of the cost function in max operation, we derive that the max operation is one piecewise quadratic function, whose expansion needs more knowledge about some unknown variables. To avoid these knowledge about unknown variables, all control input and prediction output are regarded as decision variables simultaneously, then this robust model predictive control can be formulated as a quadratic programming problem with inequality constrained condition.

Keywords: interval predictor estimation; MPC; model predictive control; robust; gradient projection; parabola property; dual optimisation; system identification; uncertainty.

DOI: 10.1504/IJSSE.2020.109738

International Journal of System of Systems Engineering, 2020 Vol.10 No.3, pp.206 - 233

Received: 22 Oct 2019
Accepted: 04 Feb 2020

Published online: 07 Sep 2020 *

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