Title Robust Data-Based Model Predictive Control for Nonlinear Constrained Systems?
Authors MANZANO CRESPO, JOSÉ MARÍA, Limón, D. , Muñoz de la Peña, D. , Calliess, J.
External publication Si
Means IFAC-PapersOnLine
Scope Conference Paper
Nature Científica
SJR Quartile 3
SJR Impact 0.29800
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
Publication date 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
Keywords Constrained optimization; Controllers; Learning systems; Machine learning; Model predictive control; Predictive control systems; Constrained systems; Data based controls; Input-to-state stability; Mac
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