Abstract

Predictive control requires a model of the system to compute the input. If the nominal model is not known, data-driven model predictive control approaches can be employed, which enables to obtain the input directly from past measured trajectories. We consider the problem of data-driven predictive control for Lur'e systems. Existing data-driven control approaches for Lur'e type systems assume that the output data of the nonlinearity is available, enabling the use of Willems’ Fundamental Lemma. We propose to utilize prior knowledge of the systems into the data collection process for Lur'e systems. The data is purposely collected in the region where the system behaves nearly linear, while the nonlinearity effects are considered as noise in the controller design. Using the tailored data, we can formulate the control problem as a semi-definite optimization problem exploiting robust control ideas. The resulting controller stabilizes the closed-loop system asymptotically and guarantees constraint satisfaction. A numerical example is conducted to illustrate the method.

DOI: https://doi.org/10.1016/j.ifacol.2024.09.034