@article{LUO2023, 
author = {Shixian LUO and Xin CHEN and Ganji HUANG},
title = {Event-Triggered Impulsive Observer-Based Stabilization for Lipschitz Nonlinear Systems with Discrete-Time Stochastic Measurement Noises},
year = {2023},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {51},
number = {11},
pages = {35-43},
keywords = {event-triggered control, impulsive observer, feedback control, stochastic noise},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.230012},
doi = {10.12141/j.issn.1000-565X.230012},
abstract = {In order to improve the utilization of computing or communication resources and reduce energy consumption, the study proposed the event-triggered impulsive observer-based output feedback control method for a class of nonlinear systems with aperiodic sampling and stochastic measurement noises. Firstly, by introducing an event-triggering mechanism that only depends on the discrete-time measurement output and an auxiliary variable, it designed a novel event-triggered impulsive observer. Then by constructing an augmented system composed of the original system and the observer error system and developing the quasi-periodic discretized Lyapunov function method, it established an ultimate bounded stability criterion in the mean square sense of the augmented closed-loop systems. The criterion reveals the influence mechanism of the sampling period, noise intensity, and event trigger parameters on system performance. Next, combined with the joint design approach, the output feedback controller synthesis problem was transformed into solving a set of LMIs based on the augmented system, thus solving the difficult problem that the state feedback gain and the observer gain cannot be separated in the presents of the stochastic measurement noise. Finally, on the Matlab platform, the performance of the proposed control method was analyzed via a connecting rod robotic arm. The experimental results demonstrate that the proposed method is effective in reducing the number of transmissions and conserving communication/computing resources. Furthermore, it successfully addresses the stabilization problems of nonlinear systems with stochastic measurement noise, thus confirming the effectiveness of the proposed approach.}
}