A variable sampling period scheduling method for networked control system under resource constraints. Issue 4 (2nd October 2019)

• Record Type: Journal Article
• Authors: Tian, Zhongda; Ren, Yi; Wang, Gang
• Is Part Of: Australian journal of electrical & electronics engineering. Volume 16:Issue 4(2019)
• Page Start: 289
• Page End: 304
• Publication Date: 2019-10-02
• Subjects: Networked control system (NCS) -- variable sampling period -- scheduling -- network utilisation -- packet transmission delay; Electrical engineering -- Periodicals; Electronics -- Periodicals; Periodicals; 621.305
• DOI: 10.1080/1448837X.2019.1661335
• Languages: English
• ISSNs: 1448-837X

Abstract

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.