title: A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty.
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.
authors.affiliation: Departamento de Engenharia Química, Escola Politécnica, Universidade Federal da Bahia, Rua Aristides Novis 2, 40210-630 Salvador-BA, Brazil. Electronic address: gsnrnr@gmail.com.
authors.forename: Gesner A
authors.initials: GA
authors.lastname: Nery
authors.affiliation: Departamento de Engenharia Química, Escola Politécnica, Universidade Federal da Bahia, Rua Aristides Novis 2, 40210-630 Salvador-BA, Brazil. Electronic address: marciomartins@ufba.br.
authors.forename: Márcio A F
authors.initials: MA
authors.lastname: Martins
authors.affiliation: Departamento de Engenharia Química, Escola Politécnica, Universidade Federal da Bahia, Rua Aristides Novis 2, 40210-630 Salvador-BA, Brazil. Electronic address: kalid@ufba.br.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Kalid
keywords: Model predictive control; Optimal tuning; Particle swarm optimization; Robust control