Robustness of Control Design via Bayesian Learning
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Abstract (arXiv)
In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search task. We seek to find a linear controller that stabilizes a one-dimensional open-loop unstable stochastic system. We compare two methods to deduce the controller: the first (deterministic) one assumes perfect knowledge of system parameter and state, the second takes into account uncertainties in both and employs Bayesian learning to compute a posterior distribution for the controller.
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References · 7
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 165
- 2019Statistical Guarantees for the Robustness of Bayesian Neural Networkscited by 35
- 2021Bayesian Inference with Certifiable Adversarial Robustnesscited by 26
- 2018Robustness Guarantees for Bayesian Inference with Gaussian Processescited by 23
- 2015Safe Control under Uncertaintycited by 2
- 2021Structured learning of safety guarantees for the control of uncertain dynamical systemscited by 1
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