Nonparameteric Event-Triggered Learning With Applications to Adaptive Model Predictive Control

• 成果类型: Article
• 署名作者: Zheng, Kaikai; Shi, Dawei; Shi, Yang; Wang, Junzheng
• 署名单位: Beijing Institute of Technology; Beijing Institute of Technology; University of Victoria
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3191760
• 发表日期: 2023
• 页码: 3469-3484
• 关键词: Adaptation models; computational modeling; predictive models; data models; Unified modeling language; State estimation; Learning systems; Adaptive model predictive control (MPC); event-based estimation; event-triggered learning; nonparameteric estimation

摘要

In this article, an event-triggered online learning problem for Lipschitz continuous systems with nonlinear model mismatch is considered, with the aim of building a data-efficient nonparameteric estimation approach for learning-based control. The system considered is composed of known linear dynamics and unknown nonlinearity, and the main focus of this work includes the design and analysis of event-triggered learning mechanisms, and the application of the learning method to adaptive model predictive control (MPC). First, a sample grid-based event-triggering mechanism and a prediction uncertainty-based event-triggering mechanisms are designed on the basis of the lazily adapted constant kinky inference framework. Then, the properties of the designed event-triggered learning methods are analyzed, and it is proved that the proposed approach provides error-bounded predictions with limited computational complexity. Third, a tube-based adaptive MPC design approach is developed utilizing the proposed event-triggered learning approach, and the closed-loop stability of the adaptive MPC is analyzed and proved based on the properties of the event-triggered learning algorithms. Implementation issues are discussed, and the effectiveness of the results is illustrated by numerical examples and comparative simulations.