Data-Driven Learning and Control With Event-Triggered Measurements

• 成果类型: Article
• 署名作者: Feng, Shilun; Shi, Dawei; Chen, Tongwen; Shi, Ling
• 署名单位: Beijing Institute of Technology; Beijing Institute of Technology; University of Alberta; Hong Kong University of Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3538798
• 发表日期: 2025
• 页码: 5301-5316
• 关键词: Event detection; system dynamics; Particle measurements; Noise measurement; noise; Linear systems; Linear matrix inequalities; Atmospheric measurements; trajectory; ELECTRONIC MAIL; Event-triggered control (ETC); learning-based control; networked control systems; sampled-data systems

摘要

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L-2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach.