Learning-Based Stochastic Model Predictive Control with State-Dependent Uncertainty
Abstract
The increasing complexity of modern systems can introduce significant uncertainties to the models that describe them, which poses a great challenge to safe model-based control. This paper presents a learning-based stochastic model predictive control (LB-SMPC) strategy with chance constraints for offset-free trajectory tracking. The LB-SMPC strategy systematically handles plant-model mismatch between the actual system dynamics and a system model via a state-dependent uncertainty term that is intended to correct model predictions at each sampling time. A chance constraint handling method is presented to ensure state constraint satisfaction to a desired level for the case of state-dependent model uncertainty. Closed-loop simulations demonstrate the usefulness of LB- SMPC for predictive control of safety-critical systems with hard-to-model and/or time-varying dynamics.
Cite
Text
Bonzanini and Mesbah. "Learning-Based Stochastic Model Predictive Control with State-Dependent Uncertainty." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Bonzanini and Mesbah. "Learning-Based Stochastic Model Predictive Control with State-Dependent Uncertainty." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/bonzanini2020l4dc-learningbased/)BibTeX
@inproceedings{bonzanini2020l4dc-learningbased,
title = {{Learning-Based Stochastic Model Predictive Control with State-Dependent Uncertainty}},
author = {Bonzanini, Angelo Domenico and Mesbah, Ali},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {571-580},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/bonzanini2020l4dc-learningbased/}
}