Convergence Guarantees for Adaptive Model Predictive Control with Kinky Inference
Abstract
We analyze the convergence properties of a robust adaptive model predictive control algorithm used to control an unknown nonlinear system. We show that by employing a standard quadratic stabilizing cost function, and by recursively updating the nominal model through kinky inference, the resulting controller ensures convergence of the true system to the origin, despite the presence of model uncertainty. We illustrate our theoretical findings through a numerical simulation.
Cite
Text
Zuliani et al. "Convergence Guarantees for Adaptive Model Predictive Control with Kinky Inference." Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024.Markdown
[Zuliani et al. "Convergence Guarantees for Adaptive Model Predictive Control with Kinky Inference." Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024.](https://mlanthology.org/l4dc/2024/zuliani2024l4dc-convergence/)BibTeX
@inproceedings{zuliani2024l4dc-convergence,
title = {{Convergence Guarantees for Adaptive Model Predictive Control with Kinky Inference}},
author = {Zuliani, Riccardo and Soloperto, Raffaele and Lygeros, John},
booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference},
year = {2024},
pages = {1058-1070},
volume = {242},
url = {https://mlanthology.org/l4dc/2024/zuliani2024l4dc-convergence/}
}