Resumen de Robust learning-based MPC for nonlinear constrained systems
José María Manzano Crespo, Daniel Limón Marruedo, David Muñoz de la Peña Sequedo, Jan Peter Calliess
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.
