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Identification of Low-Order System with Time Delay Based on Particle Swarm Optimization |
LI Minhua1, BAI Meng1, LÜ Yingjun1 |
1.Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031 |
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Abstract To solve the problem of step response identification of low-order system with time delay, a parameter estimation method based on particle swarm optimization is proposed. The method consists of the calculation of initial parameters and the parameter estimation. Firstly, an integral equation approach is utilized to estimate the initial parameters of the system with time delay. By setting an initial parameter estimation error, the parameter range of the time-delay system can be determined. Next, the particle swarm optimization algorithm is employed to reduce the influence of the measurement noise on parameter estimation. Simulation experiments are conducted to verify the performance of the proposed method in identifying the parameters of low-order system with time delay under different noisy conditions. Experimental results demonstrate that the proposed method possesses good parameter estimation precision and strong anti-noise ability and it effectively solves the step response identification problem of low-order system with time delay.
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Received: 05 March 2019
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About author:: (LI Minhua(Corresponding author), Ph.D., associate professor. Her research interests include image processing, intelligent computing and pattern recognition.)(BAI Meng, Ph.D., associate professor. His research interests include system identification, image processing and intelligent computing.)(LÜ Yingjun, master, associate professor. His research interests include design of embedded control systems.) |
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[1] KIM Y C, JIN L H. Robust Identification of Continuous-Time Low-Order Models Using Moments of a Single Rectangular Pulse Response. Journal of Process Control, 2013, 23(5): 682-695. [2] BAJARANGBALI R, MAJHI S, PANDEY S. Identification of FOPDT and SOPDT Process Dynamics Using Closed Loop Test. ISA Transactions, 2014, 53(4): 1223-1231. [3] LUO Y, CAI W, LIU H, et al. Identification of First-Order Plus Dead-Time Model from Less Step Response Data // Proc of the 9th IEEE Conference on Industrial Electronics and Applications. Wa-shington, USA: IEEE, 2014: 1410-1415. [4] LIU T, WANG Q G, HUANG H P. A Tutorial Review on Process Identification from Step or Relay Feedback Test. Journal of Process Control, 2013, 23(10): 1597-1623. [5] DU Y Y, TSAI J S H, PATIL H, et al. Indirect Identification of Continuous-Time Delay Systems from Step Responses. Applied Ma-thematical Modelling, 2011, 35(2): 594-611. [6] HUANG H P, LEE M W, CHEN C L. A System of Procedures for Identification of Simple Models Using Transient Step Response. Industrial and Engineering Chemistry Research, 2001, 40(8): 1903-1915. [7] AHMED S. Identification from Step Response-The Integral Equation Approach. The Canadian Journal of Chemical Engineering, 2016, 94(12): 2243-2256. [8] LIU T, GAO F R. A Frequency Domain Step Response Identification Method for Continuous-Time Processes with Time Delay. Journal of Process Control, 2010, 20(7): 800-809. [9] AHMED S, HUANG B, SHAH S L. Identification from Step Responses with Transient Initial Conditions. Journal of Process Control, 2008, 18: 121-130. [10] 萧德云.系统辨识理论及应用.北京:清华大学出版社, 2014. (XIAO D Y. Theory of System Identification with Applications. Beijing, China: Tsinghua University Press, 2014.) [11] BONYADI M R, MICHALEWICZ Z. Impacts of Coefficients on MovementPatterns in the Particle SwarmOptimization Algorithm. IEEE Transactions on Evolutionary Computation, 2017, 21(3): 378-390. [12] DEL VALLE Y, VENAYAGAMOORTHY G K, MOHAGHEGHI S, et al. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Transactions on Evolutionary Computation, 2008, 12(2): 171-195. [13] CHEN F W, GARNIER H, GILSON M. Robust Identification of Continuous-Time Models with Arbitrary Time-Delay from Irregularly Sampled Data. Journal of Process Control, 2015, 25: 19-27. [14] LJUNG L. Experiments with Identifications of Continuous Time Models. IFAC Proceedings Volumes, 2009, 42(10): 1175-1180. |
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