Robust Control of Uncertain Switched Affine Systems via Scenario Optimization

Negar Monir, Mahdieh S. Sadabadi, Sadegh Soudjani
Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, PMLR 283:1460-1471, 2025.

Abstract

Switched affine systems are often used to model and control complex dynamical systems that operate in multiple modes. However, uncertainties in the system matrices can challenge their stability and performance. This paper introduces a new approach for designing switching control laws for uncertain switched affine systems using data-driven scenario optimization. Instead of relaxing invariant sets, our method creates smaller invariant sets with quadratic Lyapunov functions through scenario-based optimization, effectively reducing chattering effects and regulation error. The framework ensures robustness against parameter uncertainties while improving accuracy. We validate our method with applications in multi-objective interval Markov decision processes and power electronic converters, demonstrating its versatility and effectiveness.

Cite this Paper

BibTeX
@InProceedings{pmlr-v283-monir25a, title = {Robust Control of Uncertain Switched Affine Systems via Scenario Optimization}, author = {Monir, Negar and Sadabadi, Mahdieh S. and Soudjani, Sadegh}, booktitle = {Proceedings of the 7th Annual Learning for Dynamics \& Control Conference}, pages = {1460--1471}, year = {2025}, editor = {Ozay, Necmiye and Balzano, Laura and Panagou, Dimitra and Abate, Alessandro}, volume = {283}, series = {Proceedings of Machine Learning Research}, month = {04--06 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v283/main/assets/monir25a/monir25a.pdf}, url = {https://proceedings.mlr.press/v283/monir25a.html}, abstract = {Switched affine systems are often used to model and control complex dynamical systems that operate in multiple modes. However, uncertainties in the system matrices can challenge their stability and performance. This paper introduces a new approach for designing switching control laws for uncertain switched affine systems using data-driven scenario optimization. Instead of relaxing invariant sets, our method creates smaller invariant sets with quadratic Lyapunov functions through scenario-based optimization, effectively reducing chattering effects and regulation error. The framework ensures robustness against parameter uncertainties while improving accuracy. We validate our method with applications in multi-objective interval Markov decision processes and power electronic converters, demonstrating its versatility and effectiveness.} }
Endnote
%0 Conference Paper %T Robust Control of Uncertain Switched Affine Systems via Scenario Optimization %A Negar Monir %A Mahdieh S. Sadabadi %A Sadegh Soudjani %B Proceedings of the 7th Annual Learning for Dynamics \& Control Conference %C Proceedings of Machine Learning Research %D 2025 %E Necmiye Ozay %E Laura Balzano %E Dimitra Panagou %E Alessandro Abate %F pmlr-v283-monir25a %I PMLR %P 1460--1471 %U https://proceedings.mlr.press/v283/monir25a.html %V 283 %X Switched affine systems are often used to model and control complex dynamical systems that operate in multiple modes. However, uncertainties in the system matrices can challenge their stability and performance. This paper introduces a new approach for designing switching control laws for uncertain switched affine systems using data-driven scenario optimization. Instead of relaxing invariant sets, our method creates smaller invariant sets with quadratic Lyapunov functions through scenario-based optimization, effectively reducing chattering effects and regulation error. The framework ensures robustness against parameter uncertainties while improving accuracy. We validate our method with applications in multi-objective interval Markov decision processes and power electronic converters, demonstrating its versatility and effectiveness.
APA
Monir, N., Sadabadi, M.S. & Soudjani, S.. (2025). Robust Control of Uncertain Switched Affine Systems via Scenario Optimization. Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, in Proceedings of Machine Learning Research 283:1460-1471 Available from https://proceedings.mlr.press/v283/monir25a.html.

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