Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation
Yao Xu, Renren Wang, Hongqian Lu, Xingxing Song, Yahan Deng, Wuneng Zhou · Complexity · 2021
This paper discusses the adaptive event‐triggered synchronization problem of a class of neural networks (NNs) with time‐varying delay and actuator saturation. First, in view of the limited communication channel capacity of the network system and unnecessary data transmission in the NCSs, an adaptive event‐triggered scheme (AETS) is introduced to reduce the network load and improve network utilization. Second, under the AETS, the synchronization error model of the delayed master‐slave synchronization system is constructed with actuator saturation. Third, based on Lyapunov–Krasovskii functional (LKF), a new sufficient criterion to guarantee the asymptotic stability of the synchronization error system is derived. Moreover, by solving the stability criterion expressed in the form of a set of linear matrix inequalities (LMIs), some necessary parameters of the system are obtained. At last, two examples are expressed to demonstrate the feasibility of this method.