Data-driven Nonlinear Model Predictive Control: Stability, Robustness and Offset-free Tracking

The use of data-driven and learning-based models in model predictive control (MPC) has gained an increasing popularity in recent years thanks to the growing availability of data collected in industrial plants and on the development of powerful deep learning techniques. Ihe aim of the talk is to present nonlinear MPC algorithms with guaranteed stability and robustness properties based on data-driven models of the system under control. In particular, the first part of the talk explores the design of MPC algorithms for incrementally input-to-state stable nonlinear systems modeled by recurrent neural networks (RNN), while the second part considers data-driven models in the Koopman framework, and studies how convergence to the origin can be guaranteed in presence of modeling errors.

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