Robust learning-based MPC for nonlinear constrained systems

Journal article

This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input–output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study.

Authors: Manzano, JM; Limon, D; Muñoz de la Peña, D; Calliess, J-P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Automatica
• Volume: 117
• Article number: 108948
• Publication date: 2020-03-30
• Acceptance date: 2020-03-04
• DOI: 10.1016/j.automatica.2020.108948
• ISSN: 0005-1098
• Language: English
• Keywords: Lyapunov stability; FFR; learning control; robust stability; predictive control; nonlinear systems