Online switching control with stability and regret guarantees
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Abstract (arXiv)
This paper considers online switching control with a finite candidate controller pool, an unknown dynamical system, and unknown cost functions. The candidate controllers can be unstabilizing policies. We only require at least one candidate controller to satisfy certain stability properties, but we do not know which one is stabilizing. We design an online algorithm that guarantees finite-gain stability throughout the duration of its execution. We also provide a sublinear policy regret guarantee compared with the optimal stabilizing candidate controller. Lastly, we numerically test our algorithm on quadrotor planar flights and compare it with a classical switching control algorithm, falsification-based switching, and a classical multi-armed bandit algorithm, Exp3 with batches.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2023.
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Citing papers per year: 2023: 4; 2024: 2; 2025: 1; 2026: 2.
9 papers.
- 2023Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placeboscited by 14
- 2023Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysiscited by 6
- 2025The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspectivecited by 5
- 2024Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertiacited by 2
- 2023Anytime-Competitive Reinforcement Learning with Policy Priorcited by 1
- 2024A least-square method for non-asymptotic identification in linear switching controlcited by 1
- 2023Data-Driven Adversarial Online Control for Unknown Linear Systemscited by 0
- 2026Online Learning for Supervisory Switching Controlcited by 0
- 2026Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regretcited by 0
- 2026Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret29 Jul 2026
- 2026Online Learning for Supervisory Switching Control16 Mar 2026
- 2025The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective27 Jan 2025
- 2024Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertia27 Aug 2024
- 2024A least-square method for non-asymptotic identification in linear switching control11 Apr 2024
- 2023Anytime-Competitive Reinforcement Learning with Policy Prior2 Nov 2023
- 2023Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos24 Oct 2023
- 2023Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis26 Sep 2023
- 2023Data-Driven Adversarial Online Control for Unknown Linear Systems16 Aug 2023