Subspace Predictive Control Tuning with Multiobjetive Optimization



Subspace Predictive Control, Control Tuning, Multiobjective Optimization


In this paper, a tuning method for data-driven, subspace predictive controllers is proposed. The tuning approach is based on the solution of a multiobjective optimization, in which the optimization problem is defined as the minimization of the quadratic error between closed-loop response and some desired reference trajectories. In its turn, these trajectories are described in function of user-defined time-domain objectives. The tuning parameters are obtained as the compromise solutions of the multiobjective optimization problem. Design choices and performance are discussed and the method is validated in computational simulations of some common types of process. Additionally, the tuning methodology is implemented in a multivariable pilot-scale thermoelectrical plant.


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Author Biographies

Victor Rafael Bezerra Maciel, Universidade Federal de Campina Grande

Estudante de graduação em Engenharia Elétrica com Ênfase em controle e automação na Universidade Federal de Campina Grande. Atualmente é bolsista de iniciação científica na Universidade Federal de Campina Grande (UFCG), desenvolvendo atividades nas áreas de Robótica, Controle de sistemas dinâmicos e Controle Preditivo.

Rafael Lima

Formado em Engenharia Elétrica com Ênfase em controle e automação (2010), Mestrado (2012) e Doutorado (2016) pela Universidade Federal de Campina Grande. Atua desde 2017 como professor Adjunto na UFCG desenvolvendo atividades principalmente nas áreas de Sistemas embarcados, Identificação e Controle de sistemas dinâmicos.


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How to Cite

Bezerra Maciel, V. R., & Lima, R. (2021). Subspace Predictive Control Tuning with Multiobjetive Optimization. IEEE Latin America Transactions, 20(5), 763–769. Retrieved from