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Stabilization Analysis of Stochastic Hopfield Neural Networks
Xuyang Lou,Qian Ye,Baotong Cui 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
The paper is concerned with stabilization of stochastic Hopfield neural networks (SHNNs) with time delay. First, we discuss the stabilization of SHNNs without delay in stochastic perturbation and design a stabilization controller. Then, we extend the theory to cope with the stabilization of SHNNs with delay in stochastic perturbation and two stabilization conditions are exploited.
Yanjiu Zhou,Baotong Cui,Xuyang Lou 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.2
The dynamic H∞ feedback boundary control for a class of parabolic distributed parameter systems with a non-constant (spatially varying) diffusion rate is addressed in this paper. The observer-based controller is designed to deal with non-collocated sensors and actuators, and the H∞ performance index is employed to tackle the influence of the external disturbance and measurement noise. The resulting closed-loop system is formed by the boundary actuation with the H∞ control strategy, and the output feedback is designed from the domain-averaged and boundary valued measurement, respectively. With the sufficient conditions of the linear matrix inequality that infer the stability of the system, the corresponding gains of observer and controller are solved. Numerical simulations are given to show the validity of the main results.
Integrated Design of Event-triggered Control and Mobile Non-collocated SANs for a Diffusion Process
Zhengxian Jiang,Bo Zhuang,Xuyang Lou,Wei Wu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.9
This paper is concerned with the integrated design of event-triggered control and mobile non-collocated sensor and actuator networks for a diffusion process. Firstly, an estimator is designed to estimate the states of the diffusion process and the mobile strategies of the sensors are given. Then, event-triggered control strategies are presented aiming at the benefit of saving the limited network resources. Based on the event-triggering mechanism, the value of estimation states will be sent to the controllers and the mobile actuators will move to the designated positions within the respective spatial domain. Thirdly, by using the Lyapunov functional approach, sufficient conditions are established to guarantee the boundedness of the event-triggered control system. Moreover, the existence of the lower bound of minimum inter-event time is also proved to exclude the Zeno behavior. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed results.