Year: 2025
Type: conference-paper
Abstract: Understanding the behavior of black-box data-driven controllers is a key challenge in modern control design. In this work, we propose an eXplainable AI (XAI) methodology based on Inverse Optimal Control (IOC) to obtain local explanations for the behavior of a controller operating around a given region. Specifically, we extract the weights assigned to tracking errors and control effort in the implicit cost function that a black-box controller is optimizing, offering a more transparent and interpretable representation of the controller’s underlying objectives. This approach presents connections with well-established XAI techniques, such as Local Interpretable Model-agnostic Explanations (LIME) since it is still based on a local approximation of the control policy. However, the explanation provided by our method offers a structured and more control-relevant perspective. Numerical examples demonstrate that the inferred cost function consistently provides a deeper understanding of the controller’s decision-making process.
Institutions: Politecnico di Milano, University of Minnesota
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G4183413234
OpenAlex ID: W7123353622