# eXplainable AI for data driven control: an inverse optimal control approach

arXiv: https://arxiv.org/abs/2504.11446 · HTML: https://mlsanity.com/paper/2504.11446/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control](https://mlsanity.com/paper/2605.10624/) (2026)

## Abstract (arXiv)

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, rather being limited to a standard sensitivity analysis, the explanation provided by our method relies on the solution of an inverse Linear Quadratic (LQ) problem, offering 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, shedding light on otherwise counterintuitive or unexpected phenomena.