Learning-based MPC of sampled-data systems with partially unknown dynamics.

Abstract

In this paper, a novel learning-based model predictive control (LMPC) method is proposed for sampled-data control systems with partially unknown dynamics. Many real-world processes are subject to time-varying parameters and irregular data sampling, making accurate modeling and stability guarantees extremely challenging. To address this, the proposed method uses a neural ordinary differential equation (NODE) to learn unknown time-varying parameter dynamics from irregularly observed data. This learned model is then integrated into the sampled-data MPC framework. In particular, the LMPC method guarantees the system's ultimate boundedness by deriving conditions based on the Gronwall-Bellman inequality. Finally, two practical examples illustrate the applicability of the LMPC method to real-world systems and demonstrate its quantitative stability analysis.