Safety-Critical Randomized Event-Triggered Learning of Gaussian Process With Applications to Data-Driven Predictive Control

• 成果类型: Article
• 署名作者: Zheng, Kaikai; Shi, Dawei; Shi, Yang
• 署名单位: Beijing Institute of Technology; University of Victoria
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3523682
• 发表日期: 2025
• 页码: 3920-3935
• 关键词: trajectory; safety; Event detection; predictive models; uncertainty; data models; computational modeling; Adaptation models; accuracy; training; Data-driven control; event-triggered learning; Predictive control; randomized learning; safety-critical system

摘要

Safety and data efficiency are important concerns in data-driven control, especially for nonlinear systems with unknown dynamics and subject to disturbances. In this work, we consider a class of control-affine nonlinear systems with partially unknown dynamics and aim to introduce an event-triggered learning-based control approach with guaranteed safety and improved data utilization efficiency. Specifically, a randomized learning approach is employed to evaluate the safety of state trajectories by defining and estimating its confidence interval, with data from a multisample of randomly generated state trajectories. Using the proposed randomized learning algorithm, a nominal trajectory with a high probability safety guarantee is designed, thus ensuring the disturbed system states to remain within a prespecified range around the nominal trajectory with a sufficiently high probability. Through removing irrelevant data, a local prediction model around the nominal trajectory is learned with satisfactory precision, and is updated online using an event-triggered learning strategy. Based on the learned model, an efficient data-driven predictive controller is designed to force the system states to evolve within the vicinity of the designed safety nominal trajectory. The effectiveness of the proposed event-triggered learning and data-driven control approaches is validated through comprehensive simulation studies.