Time-delay compensation method for networked control system based on time-delay prediction and implicit PIGPC

被引:26
作者
Tian Z.-D. [1 ]
Gao X.-W. [2 ]
Gong B.-L. [2 ]
Shi T. [3 ]
机构
[1] School of Information Science and Engineering, Shenyang University of Technology, Shenyang
[2] School of Information Science and Engineering, Northeastern University, Shenyang
[3] Department of Humanities, Liaoning Forestry Vocation-Technical College, Shenyang
基金
中国国家自然科学基金;
关键词
implicit proportional-integral-based generalized predictive controller (PIGPC); least squares support vector machine (LSSVM); Networked control system; particle swarm optimization (PSO); time-delay compensation;
D O I
10.1007/s11633-015-0897-7
中图分类号
学科分类号
摘要
A network time-delay compensation method based on time-delay prediction and implicit proportional-integral-based generalized predictive controller (PIGPC) is proposed. The least squares support vector machine (LSSVM) is used to predict the current time-delay, the parameters of the least squares support vector machine are optimized by particle swarm optimization (PSO) algorithm, and the predicted time-delay is used instead of the actual time-delay as the parameters of the network time-delay compensation controller. In order to improve the compensation effect of implicit generalized predictive controller (GPC), this paper puts forward an implicit generalized predictive control algorithm with proportional-integral-based (PI) structure and designs the controller based on implicit PIGPC. Through the simulation results, the effectiveness of this design in the paper is verified. © 2015, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.
引用
收藏
页码:648 / 656
页数:8
相关论文
共 18 条
[1]  
Tian Z.D., Gao X.W., Li K., A hybrid time-delay prediction method for networked control system, International Journal of Automation and Computing, 11, 1, pp. 19-24, (2014)
[2]  
Vatanski N., Georges J.P., Aubrun C., Rondeau E., Jamsa-Jounela S.L., Networked control with delay measurement and estimation, Control Engineering Practice, 17, 2, pp. 231-244, (2009)
[3]  
Li W.L., Zhang X.B., Li H.M., Co-simulation platforms for co-design of networked control systems: An overview, Control Engineering Practice, 23, pp. 44-56, (2014)
[4]  
Zhang C.J., Gu J., Robust fault detection filter for nonlinear state-delay networked control system, International Journal of Automation and Control, 6, 3-4, pp. 215-230, (2012)
[5]  
Shi Y., Huang J., Yu B., Robust tracking control of networked control systems: Application to a networked DC motor, IEEE Transactions on Industrial Electronics, 60, 12, pp. 5864-5874, (2013)
[6]  
Chen Z.S., He Y., Wu M., Robust fuzzy tracking control for nonlinear networked control systems with integral quadratic constraints, International Journal of Automation and Computing, 7, 4, pp. 492-499, (2010)
[7]  
House T., Keeling M.J., Epidemic prediction and control in clustered populations, Journal of Theoretical Biology, 272, 1, pp. 1-7, (2011)
[8]  
Yang R.N., Liu G.P., Shi P., Thomas C., Basin M.V., Predictive output feedback control for networked control systems, IEEE Transactions on Industrial Electronics, 61, 1, pp. 512-520, (2014)
[9]  
Fu W., Yang X.Y., Feng W., Liu G.Q., Predictive control compensation for time delay in networked control systems, Systems Engineering and Electronics, 33, 9, pp. 2066-2071, (2011)
[10]  
Song H.B., Liu G.P., Yu L., Networked predictive control of uncertain systems with multiple feedback channels, IEEE Transactions on Industrial Electronics, 60, 11, pp. 5228-5238, (2013)