TY - JOUR
T1 - Event-triggered learning-based control for output tracking with unknown cost functions
AU - Song, Jiliang
AU - Shi, Dawei
AU - Tang, Shu Xia
AU - Yu, Hao
AU - Shi, Yang
N1 - Publisher Copyright:
© 2025
PY - 2025/6
Y1 - 2025/6
N2 - In this paper, a two-layer event-triggered learning-based control framework is proposed to address extremum seeking problem in networked control systems with limited communication resources and unknown cost function. In this framework, the lower layer is an event-triggered controller to drive the output to track the given setpoints generated from the upper layer, where a learning-based optimizer is developed to approach the extremum of the unknown cost function. Specifically, in the lower layer, an event-triggered output controller, based on a high-gain extended state observer, is designed to tackle uncertainties and disturbances. In the upper layer, a nonparametric gradient model is established, and then the gradient descent method is applied to generate setpoints for the tracking control. The update of the learning and optimization process is determined by the tracking performance of the lower layer. The stability and Zeno-freeness of the proposed event-triggered controller is proved. Furthermore, the dependence of the convergence rate of the proposed learning-based extremum seeking algorithm on the designed parameters is also explicitly characterized. Finally, the effectiveness of the proposed framework is validated by numerical examples.
AB - In this paper, a two-layer event-triggered learning-based control framework is proposed to address extremum seeking problem in networked control systems with limited communication resources and unknown cost function. In this framework, the lower layer is an event-triggered controller to drive the output to track the given setpoints generated from the upper layer, where a learning-based optimizer is developed to approach the extremum of the unknown cost function. Specifically, in the lower layer, an event-triggered output controller, based on a high-gain extended state observer, is designed to tackle uncertainties and disturbances. In the upper layer, a nonparametric gradient model is established, and then the gradient descent method is applied to generate setpoints for the tracking control. The update of the learning and optimization process is determined by the tracking performance of the lower layer. The stability and Zeno-freeness of the proposed event-triggered controller is proved. Furthermore, the dependence of the convergence rate of the proposed learning-based extremum seeking algorithm on the designed parameters is also explicitly characterized. Finally, the effectiveness of the proposed framework is validated by numerical examples.
KW - Event-triggered control
KW - Extremum seeking
KW - Gaussian processes
KW - Network control system
UR - http://www.scopus.com/inward/record.url?scp=85219157039&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2025.112235
DO - 10.1016/j.automatica.2025.112235
M3 - Article
AN - SCOPUS:85219157039
SN - 0005-1098
VL - 176
JO - Automatica
JF - Automatica
M1 - 112235
ER -