Approximate Explicit Model Predictive Control via Piecewise Nonlinear System Identification

8 Pages Posted: 26 Jan 2019

See all articles by Van Vuong Trinh

Van Vuong Trinh

University Grenoble Alpes - Grenoble Images Parole Signal Automatique (GIPSA Lab); University Grenoble Alpes - CNRS, GIPSA-lab

Mazen Alamir

University Grenoble Alpes - Grenoble Images Parole Signal Automatique (GIPSA Lab)

Patrick Bonnay

University Grenoble Alpes - CNRS, GIPSA-lab

Date Written: January 14, 2019

Abstract

This article presents an identification methodology to capture general relationships, with application to piecewise nonlinear approximations of model predictive control for constrained (non)linear systems. The mathematical formulation takes, at each iteration, the form of a constrained linear (or quadratic) optimization problem that is mathematically feasible as well as numerically tractable. The efficiency of the devised methodology is demonstrated via two industrial applications. Results suggest the possibility to achieve high approximate precision with limited number of regions, leading to a significant reduction in computation time when compared to the state-of-the-art implicit model predictive control solvers.

Keywords: Explicit Model Predictive Control, Nonlinear Approximation, System Identification

Suggested Citation

Trinh, Van Vuong and Alamir, Mazen and Bonnay, Patrick, Approximate Explicit Model Predictive Control via Piecewise Nonlinear System Identification (January 14, 2019). Available at SSRN: https://ssrn.com/abstract=3315784 or http://dx.doi.org/10.2139/ssrn.3315784

Van Vuong Trinh (Contact Author)

University Grenoble Alpes - Grenoble Images Parole Signal Automatique (GIPSA Lab) ( email )

France

University Grenoble Alpes - CNRS, GIPSA-lab ( email )

France

Mazen Alamir

University Grenoble Alpes - Grenoble Images Parole Signal Automatique (GIPSA Lab) ( email )

France

Patrick Bonnay

University Grenoble Alpes - CNRS, GIPSA-lab ( email )

France

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