Event-triggered adaptive control for delayed memristive neural networks with unknown parameters and external disturbances

Abstract

The synchronisation problem is studied for master–slave memristive neural networks (MNNs) in this paper. For alleviating the burden of communication bandwidth, a novel event-triggered scheme of data transmission is designed in the sensor-to-controller (S-C) channel. To deal with the unknown parameters and disturbances of master–slave MNNs, the adaptive controller is designed with the system states of triggering instants. Different from existing results about event-triggered adaptive control (ETAC) for MNNs, in which the event-triggered mechanism (ETM) is installed in the controller-to-actuator (C-A) channel, the event-triggered scheme in this paper is designed between the sensor and the controller, so the information flow of S-C channel is discontinuous. The adaptive laws can only use discrete-time system states transmitted at triggering instants to update control gains in this paper. By means of the Lyapunov methods, adaptive control theories and event-triggered techniques, sufficient conditions for synchronisation and quasi-synchronisation are obtained. At the same time, the designed ETM can avoid Zeno behaviour theoretically. Finally, the validity of the obtained results is shown by two simulation examples.

Keywords:

• Adaptive control
• event-triggered control
• memristive neural networks
• zeno behaviour
• S-C channel

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Nature Science Foundation of China [grant number 11971444].

Notes on contributors

Zhenning Zhang

Zhenning Zhang received his B.S. degree from the School of Mathematics and Statistics of Zhengzhou University in 2020. He is currently working toward the M.S. degrees in the School of Mathematics and Statistics of Zhengzhou University. His current research interests include stochastic systems and networked control systems.

Xiaowu Mu

Xiaowu Mu received his B.S., M.S. and Ph.D. degrees from the Department of Mathematics of Peking University in 1983, 1988 and 1991, respectively. Currently, he is a Professor in Zhengzhou University. His research interests include stochastic systems, hybrid systems, nonlinear control, and networked control systems.

Zenghui Hu

Zenghui Hu received his Ph.D. degree from the School of Mathematics and Statistics of Zhengzhou University in 2022. Currently, he is an associate research fellow in Zhengzhou University. His current research interests include the networked control systems and stochastic hybrid systems.