title: A swarm intelligence-based tuning method for the Sliding Mode Generalized Predictive Control.
abstract: This work presents an automatic tuning method for the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Quadratic Programming (QP), thus yielding an online dual optimization scheme. Simulations and performance indexes for common process models in industry, such as nonminimum phase and time delayed systems, result in a better performance, improving robustness and tracking accuracy.
authors.affiliation: INESC TEC - INESC Technology and Science (formerly INESC Porto, UTAD pole) Department of Engineering, School of Sciences and Technology 5001-811 Vila Real, Portugal. Electronic address: josenalde@eaj.ufrn.br.
authors.forename: J B
authors.initials: JB
authors.lastname: Oliveira
authors.affiliation: INESC TEC - INESC Technology and Science (formerly INESC Porto, UTAD pole) Department of Engineering, School of Sciences and Technology 5001-811 Vila Real, Portugal. Electronic address: jboavent@utad.pt.
authors.forename: J
authors.initials: J
authors.lastname: Boaventura-Cunha
authors.affiliation: INESC TEC - INESC Technology and Science (formerly INESC Porto, UTAD pole) Department of Engineering, School of Sciences and Technology 5001-811 Vila Real, Portugal. Electronic address: oliveira@utad.pt.
authors.forename: P B
authors.initials: PB
authors.lastname: Moura Oliveira
authors.affiliation: INESC TEC - INESC Technology and Science (formerly INESC Porto, UTAD pole) Department of Engineering, School of Sciences and Technology 5001-811 Vila Real, Portugal. Electronic address: freireh@gmail.com.
authors.forename: H
authors.initials: H
authors.lastname: Freire
keywords: Model Predictive Control; Particle Swarm Optimization; Robustness; Sliding Mode; Soft computing