Event-Triggered Adaptive Antidisturbance Switching Control for Switched Systems With Dynamic Neural Network Disturbance Modeling

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 12; pp. 17688 - 17700
• Main Authors: Zhao, Ying, Gao, Yuxuan, Sang, Hong, Fu, Jun, Li, Yuzhe
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2024
• Subjects: Adaptation models; Adaptive antidisturbance; Control systems; Disturbance observers; dynamic event-triggered; dynamic neural network; multisource disturbances; Neural networks; Nonlinear dynamical systems; Switched systems; Switches
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2023.3307389

In this article, a dynamic event-triggered adaptive antidisturbance (ETAAD) switching control strategy is proposed for switched systems subject to multisource disturbances. The disturbances are divided into two categories: the available unmodeled disturbance and the unavailable dynamic neural network modeled disturbance. First, a dynamic ET criterion is set based on the system state. Then, a novel dynamic ETA disturbance estimator is introduced to observe the modeled disturbance. Furthermore, according to the ET rule and adaptive disturbance observer, a switched controller is designed. Next, under the controller and switching criterion with the average dwell time limitation, sufficient conditions are given to force the switched systems to realize multisource disturbance suppression (DS), trajectory tracking, and communication resource (CR) saving simultaneously. Meanwhile, the Zeno phenomenon may be caused by the ET rule being excluded. In addition, the presented ETAAD approach is also applicable to the nonswitched systems case. Finally, a simulation case is given to validate the effectiveness of the dynamic ETAAD switching control method.