title: Stabilization of Neural-Network-Based Control Systems via Event-Triggered Control With Nonperiodic Sampled Data.
abstract: This paper focuses on a problem of event-triggered stabilization for a class of nonuniformly sampled neural-network-based control systems (NNBCSs). First, a new event-triggered data transmission mechanism is designed based on the nonperiodic sampled data. Different from the previous works, the proposed triggering scheme enables the NNBCSs design to enjoy the advantages of both nonuniform and event-triggered sampling schemes. Second, under the nonperiodic event-triggered data transmission scheme, the nonperiodic sampled-data three-layer fully connected feedforward neural-network (TLFCFFNN)-based event-triggered controller is constructed, and the resulting closed-loop TLFCFFNN-based event-triggered control system is modeled as a state delay system based on time-delay system modeling approach. Then, the stability criteria for the closed-loop system is formulated using Lyapunov-Krasovskii functional approach. Third, the sufficient conditions for the codesign of the TLFCFFNN-based controller and triggering parameters are given in terms of solvability of matrix inequalities to guarantee the asymptotical stability of the closed-loop system and an upper bound on the given cost function while reducing the updates of the controller. Finally, three numerical examples are provided to illustrate the effectiveness and benefits of the proposed results.
authors.forename: Songlin
authors.initials: S
authors.lastname: Hu
authors.forename: Dong
authors.initials: D
authors.lastname: Yue
authors.forename: Xiangpeng
authors.initials: X
authors.lastname: Xie
authors.forename: Yong
authors.initials: Y
authors.lastname: Ma
authors.forename: Xiuxia
authors.initials: X
authors.lastname: Yin