Online learning constrained model predictive controller based on double prediction

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.

Automated summary

The study introduces a data-based predictive controller that offers both robust stability guarantees and online learning capabilities by employing a double-prediction approach. The combination of safe prediction and online learning prediction ensures the safety of the controlled system and allows for incremental learning over time. Sufficient conditions for robust stability and constraint satisfaction are provided, with illustrations given in a simulated case study.