Online learning constrained model predictive control based on double prediction. (7th September 2020)

• Record Type: Journal Article
• Authors: Manzano, J. M.; Muñoz de la Peña, D.; Calliess, J.; Limon, D.
• Other Names: Müller Matthias A. guestEditor.; Allgöwer Frank guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 18(2021)
• Page Start: 8813
• Page End: 8829
• Publication Date: 2020-09-07
• Subjects: data‐based control -- learning‐based MPC -- nonlinear MPC -- robust control; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5124
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: A data‐based predictive controller is proposed, offering both robust stability guarantees and online learning capabilities. To merge these two properties in a single controller, a double‐prediction approach is taken. On the one hand, a safe prediction is computed using Lipschitz interpolation on the basis of an offline identification dataset, which guarantees safety of the controlled system. On the other hand, the controller also benefits from the use of a second online learning‐based prediction as measurements incrementally become available over time. Sufficient conditions for robust stability and constraint satisfaction are given. Illustrations of the approach are provided in a simulated case study.