Daniel Limon
Associated Professor at the Department for Systems Engineering and Automation of the University of Seville
This talk is devoted to present recent results on the combination of Model Predictive Control with learning methods to derive data-driven controllers. The presented stabilizing Model Predictive Controllers (MPC) are based on prediction models that are inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called LACKI, the estimated (possibly nonlinear) model function together with an estimation of Hölder constant is provided. Based on this prediction model, two different data-driven control scenarios are studied. The first scenario consists in a prediction model learned offline and used to design a stabilizing MPC. In the second scenario, the model is updated on-line with new input-output data obtained from feedback to enhance the quality of the predictions. In this case, a novel stabilizing MPC controller is proposed.
Daniel Limon is Associate Professor at the Department of Systems Engineering and Automation of the University of Seville (Spain) and he is the responsible of the research group on Estimation, Prediction, Optimization and Control. He is the author or coauthor of more than 100 publications including book chapters, journal papers, conference proceedings and educational books. He was Keynote Speaker at the International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control in 2008 and Semiplenary Lecturer at the IFAC Conference on Nonlinear Model Predictive Control in 2012 (NMPC’12). He has been the Chair of the 5th IFAC Conference on Nonlinear Model Predictive Control (NMPC’15). He has cofunded the spin off company Optimal Performance (University of Seville, Spain). His current research interests include Model Predictive Control, economic process control, trajectory tracking control and data-driven control.