A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty

Gesner A Nery Jr; Márcio A F Martins; Ricardo Kalid

doi:10.1016/j.isatra.2013.12.019

A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty

ISA Trans. 2014 Mar;53(2):560-7. doi: 10.1016/j.isatra.2013.12.019. Epub 2014 Jan 4.

Authors

Gesner A Nery Jr¹, Márcio A F Martins², Ricardo Kalid³

Affiliations

¹ Departamento de Engenharia Química, Escola Politécnica, Universidade Federal da Bahia, Rua Aristides Novis 2, 40210-630 Salvador-BA, Brazil. Electronic address: gsnrnr@gmail.com.
² Departamento de Engenharia Química, Escola Politécnica, Universidade Federal da Bahia, Rua Aristides Novis 2, 40210-630 Salvador-BA, Brazil. Electronic address: marciomartins@ufba.br.
³ Departamento de Engenharia Química, Escola Politécnica, Universidade Federal da Bahia, Rua Aristides Novis 2, 40210-630 Salvador-BA, Brazil. Electronic address: kalid@ufba.br.

PMID: 24398055
DOI: 10.1016/j.isatra.2013.12.019

Abstract

This paper describes the development of a method to optimally tune constrained MPC algorithms with model uncertainty. The proposed method is formulated by using the worst-case control scenario, which is characterized by the Morari resiliency index and the condition number, and a given nonlinear multi-objective performance criterion. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a modified version of the particle swarm optimization technique, because of its effectiveness in dealing with this kind of problem. The performance of this PSO-based tuning method is evaluated through its application to the well-known Shell heavy oil fractionator process.

Keywords: Model predictive control; Optimal tuning; Particle swarm optimization; Robust control.