Fault-tolerant Quantized Control for Switched Neural Networks with Actuator Faults and Dynamic Output Quantization

作     者：Su, Yue Wang, Xinrui Tai, Weipeng Zhou, Jianping 

作者机构：School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Chengdu University of Technology Chengdu610051 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China 

出 版 物：《IAENG International Journal of Applied Mathematics》 (IAENG Int. J. Appl. Math.)

年 卷 期：2025年第55卷第1期

页      面：7-15页

核心收录：

主　　题：Lyapunov functions 

摘      要：This paper examines fault-tolerant quantized control for neural networks under persistent dwell-time switching, considering the presence of actuator faults and dynamic output quantization. The dynamic scaling factor (DSF) of the quantizer is designed as a piecewise function concerning the output to avoid the possibility of division by zero. To reduce conservatism, the controller is designed to combine the system model with a time scheduler constructed with a minimum time span. A sufficient condition for the asymptotic stability and L2-gain of the closed-loop system is derived using a piecewise Lyapunov functional and decoupling approach. When the condition is satisfied, the needed feedback gains and the parameter range associated with the DSF can be determined by exact mathematical expressions. For comparison, feedback gains that depend only on the system mode are also studied, and the corresponding design method is presented. The numerical simulation results demonstrate the effectiveness of the proposed control scheme. © (2025), (International Association of Engineers). All rights reserved.