Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty

Angelo Domenico Bonzanini, Ali Mesbah
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:571-580, 2020.

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 this Paper

BibTeX
@InProceedings{pmlr-v120-bonzanini20a, 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}, pages = {571--580}, year = {2020}, editor = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie}, volume = {120}, series = {Proceedings of Machine Learning Research}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/bonzanini20a/bonzanini20a.pdf}, url = {https://proceedings.mlr.press/v120/bonzanini20a.html}, 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.} }
Endnote
%0 Conference Paper %T Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty %A Angelo Domenico Bonzanini %A Ali Mesbah %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-bonzanini20a %I PMLR %P 571--580 %U https://proceedings.mlr.press/v120/bonzanini20a.html %V 120 %X 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.
APA
Bonzanini, A.D. & Mesbah, A.. (2020). Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 120:571-580 Available from https://proceedings.mlr.press/v120/bonzanini20a.html.

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