Data-Driven Safe Control of Uncertain Linear Systems Under Aleatory Uncertainty

• 成果类型: Article
• 署名作者: Modares, Hamidreza
• 署名单位: Michigan State University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3267019
• 发表日期: 2024
• 页码: 551-558
• 关键词: chance constraints; Data-driven control; probabilistic safe control

摘要

Safe control of constrained uncertain linear systems under aleatory uncertainty is considered. Aleatory uncertainty characterizes random noises and is modeled by a probability distribution function (pdf). Data-based probabilistic safe controllers are designed for the cases where the noise pdf is 1) zero-mean Gaussian with a known covariance, 2) zero-mean Gaussian with an uncertain covariance, and 3) zero-mean non-Gaussian with an unknown distribution. Easy-to-check-model-based conditions for guaranteeing probabilistic safety are provided for the first case by introducing probabilistic $\lambda$-contractive sets. These results are then extended to the second and third cases by leveraging distributionally-robust probabilistic safe control and conditional-value-at-risk-based probabilistic safe control, respectively. Data-based implementations of these probabilistic safe controllers are then considered. Moreover, an upper bound on the minimal risk level, under which the existence of a safe controller is guaranteed, is learned using collected data. A simulation example is provided to show the effectiveness of the proposed approach.