Título Robust Data-Based Model Predictive Control for Nonlinear Constrained Systems?
Autores MANZANO CRESPO, JOSÉ MARÍA, Limón, D. , Muñoz de la Peña, D. , Calliess, J.
Publicación externa Si
Alcance Conference Paper
Naturaleza Científica
Cuartil SJR 3
Impacto SJR 0.298
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056884220&doi=10.1016%2fj.ifacol.2018.11.039&partnerID=40&md5=f062ff78346b3065f79b06cb56cf1c88
Fecha de publicacion 01/01/2018
ISI 000451092800078
Scopus Id 2-s2.0-85056884220
DOI 10.1016/j.ifacol.2018.11.039
Abstract This paper presents stabilizing Model Predictive Controllers (MPC) to be applied to black-box systems subject to constraints in the inputs and the outputs. The prediction model of the controllers is inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called SPKI, the estimated (possibly nonlinear) model function is provided. Based on this, a predictive controller with stability guaranteed by design is proposed. Robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem but without adding a terminal constraint on the optimisation problem. The proposed predictive controller has been validated in a simulation case study. © 2018
Palabras clave Constrained optimization; Controllers; Learning systems; Machine learning; Model predictive control; Predictive control systems; Constrained systems; Data based controls; Input-to-state stability; Machine learning techniques; Model predictive controllers; Optimisation problems; Predictive controller; Terminal constraint; Predictive analytics
Miembros de la Universidad Loyola

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