A variable sampling period scheduling method for networked control system under resource constraints

ABSTRACT

In order to improve the performance of networked control system, a variable sampling period scheduling method for networked control system under resource constraints is presented based on network operation state. First, the scheduler obtains the current and past network utilisation, and packet transmission delay online. Based on the acquired network state, the improved particle swarm optimisation algorithm is used to optimise Gaussian process regression model to predict the network utilisation and packet transmission delay in the next sampling period. According to the error and error change rate of system control loop, the weight of the control loop is calculated based on fuzzy rules. Then, in accordance with the needs of network performance and control performance, the network bandwidth is allocated based on the predictive value of network utilisation and packet transmission delay. Finally, the new sampling period of each control loop is calculated. The simulation experiments are performed out based on True time toolbox. The simulation results show that the proposed variable sampling period scheduling methodcan improve output control performance of the system, reduce the packet transmission delay and integral absolute error value of the control loops, and improve network utilisation. The overall control performance of the system is improved. The variable sampling period scheduling method in this paper is effective.

KEYWORDS:

• Networked control system (NCS)
• variable sampling period
• scheduling
• network utilisation
• packet transmission delay

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Science Research Project of Liaoning Education Department: [Grant Number LGD2016009] and the Key Project of Natural Science Foundation of Liaoning Province of China: [Grant Number 20170540686];Natural Science Foundation of Liaoning Province [20170540686].

Notes on contributors

Zhongda Tian

Zhongda Tian He received the Ph. D degree in Control Theory and Control Engineering from Northeastern University, China in 2013. He is currently an Associate Professor in College of Information Science and Engineering, Shenyang University of Technology, China. His research interests include predictive control, delay compensation and scheduling for networked control system, time series prediction.

Yi Ren

Yi Ren He received his B. Eng degree from Liaoning University of Science and Technology in 2016. He is currently a Master student of Control Theory and Control Engineering, College of Information Science and Engineering, Shenyang University of Technology, China. His interest is scheduling method for networked control system.

Gang Wang

Gang Wang He received his B. Eng degree from Shanxi Datong University in 2016. He is currently a Master student of Control Theory and Control Engineering, College of Information Science and Engineering, Shenyang University of Technology, China. His interest is time series prediction.