Synchronization of Delayed Neural Networks With Actuator Failure Based on Stochastic Sampled-Data Controller

• Published in: IEEE access Vol. 8; pp. 200923 - 200931
• Main Authors: Tian, Jiaping, Zhang, Jiayong, Liu, Yajuan, Ge, Chao, Hua, Changchun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuator failure; Actuators; Controllers; Convexity; Delays; Failure analysis; Linear matrix inequalities; linear matrix inequalities (LMIs); Master-slave; Neural networks; Optimization; Random sampling; Stability analysis; Stability criteria; Stochastic sampling; Synchronism; Synchronization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3033808

This paper addresses the master-slave synchronization problems of delayed neural networks with actuator failure based on stochastic sampled-data controller. To simplify the analysis process, only two different sampling periods whose occurrence probabilities follow the Bernoulli distribution are considered. In addition, it can be further extended to cases with multiple random sampling periods. The sampling system with random parameters is transformed into a continuous system through applying the input delay method. The novelty of this article is to consider the problem of actuator failure which may exist in the real world. By constructing a new type of Lyapunov-Krasovskii function (LKF), a sampling controller for neural networks synchronization system is designed. Using Jensens's inequality, Wirtinger's inequality and convex optimization methods, the stability criterion of neural networks with low conservativeness is acquired. Meanwhile, the controller gain matrix can be obtained through solving the linear matrix inequalities (LMIs). One numerical example provides feasibility and advantages of theoretical results.