Multi-linear model set design based on the nonlinearity measure and H-gap metric

Davood Shaghaghi; Alireza Fatehi; Ali Khaki-Sedigh

doi:10.1016/j.isatra.2017.01.021

Multi-linear model set design based on the nonlinearity measure and H-gap metric

ISA Trans. 2017 May:68:1-13. doi: 10.1016/j.isatra.2017.01.021. Epub 2017 Feb 10.

Authors

Davood Shaghaghi¹, Alireza Fatehi², Ali Khaki-Sedigh³

Affiliations

¹ APAC Research Group, Industrial Control Center of Excellence, Faculty of Electrical Engineering, K. N. Toosi University of Technology, 16317-14191 Tehran, Iran. Electronic address: davood.shaghaghi@ee.kntu.ac.ir.
² APAC Research Group, Industrial Control Center of Excellence, Faculty of Electrical Engineering, K. N. Toosi University of Technology, 16317-14191 Tehran, Iran. Electronic address: fatehi@kntu.ac.ir.
³ APAC Research Group, Industrial Control Center of Excellence, Faculty of Electrical Engineering, K. N. Toosi University of Technology, 16317-14191 Tehran, Iran. Electronic address: sedigh@kntu.ac.ir.

PMID: 28190564
DOI: 10.1016/j.isatra.2017.01.021

Abstract

This paper proposes a model bank selection method for a large class of nonlinear systems with wide operating ranges. In particular, nonlinearity measure and H-gap metric are used to provide an effective algorithm to design a model bank for the system. Then, the proposed model bank is accompanied with model predictive controllers to design a high performance advanced process controller. The advantage of this method is the reduction of excessive switch between models and also decrement of the computational complexity in the controller bank that can lead to performance improvement of the control system. The effectiveness of the method is verified by simulations as well as experimental studies on a pH neutralization laboratory apparatus which confirms the efficiency of the proposed algorithm.

Keywords: Generalized predictive control; H-gap metric; Model bank selection; Multiple-model controller; Nonlinearity measure.