Modeling and Control of Networked Control Systems with Partitioned Random Delay

Authors

Yicai Liu, 1.Institute of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; ;2.Department of Mechatronics Engineering and Automotive Services, Wuhan Business University, Wuhan 430056, China;
Bin Liu, 1.Institute of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; ;
Anwei Shi, 1.Institute of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; ;

Abstract

Abstract: As for a class of networked control systems which have the bilateral random delay of sensor-to- controller and controller-to-actuator, the networked control systems were modeled as discrete-time jump systems with uncertain parameters by firstly partitioning the maximum possible delay value into a plurality of equal intervals, and then describing it as a stochastic process of a finite state Markov chain according to the jump characteristics of the delay in each interval. Considering the condition that the elements of the transition probability matrix of the Markov chain were partly unknown, the time-varying controller meeting the requirement of system stability was designed based on the theory of Lyapunov stability and stochastic process. Lastly, the simulation examples illustrate the effectiveness of the proposed method.

Recommended Citation

Liu, Yicai; Liu, Bin; and Shi, Anwei (2019) "Modeling and Control of Networked Control Systems with Partitioned Random Delay," Journal of System Simulation: Vol. 30: Iss. 2, Article 35.
DOI: 10.16182/j.issn1004731x.joss.201802035
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss2/35

First Page

654

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201802035

Last Page

663

CLC

TP273

Recommended Citation

Liu Yicai, Liu Bin, Shi Anwei. Modeling and Control of Networked Control Systems with Partitioned Random Delay[J]. Journal of System Simulation, 2018, 30(2): 654-663.

DOI

10.16182/j.issn1004731x.joss.201802035