Abstract and Applied Analysis

PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

Yuanchang Zhong, Xu Huang, Pu Meng, and Fachuan Li

Full-text: Open access

Abstract

The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor) of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

Article information

Source
Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 731368, 7 pages.

Dates
First available in Project Euclid: 2 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412278505

Digital Object Identifier
doi:10.1155/2014/731368

Zentralblatt MATH identifier
07022968

Citation

Zhong, Yuanchang; Huang, Xu; Meng, Pu; Li, Fachuan. PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 731368, 7 pages. doi:10.1155/2014/731368. https://projecteuclid.org/euclid.aaa/1412278505


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References

  • C. Gongjian, S. Fengbin, W. Lili et al., “Analysis on natural gas pressure regulator design principle and it's influence factors,” Natural Gas and Oil, vol. 29, no. 3, pp. 67–71, 2011.
  • F. Liang, J. Di, and L. Shuhui, “Study on dynamic model of single-stage self-operated gas regulator,” Gas and Heat, vol. 29, no. 2, pp. B10–B13, 2009.
  • C. Yudong, W. Yong, and L. Yanjun, “Overview of control valve technology,” Control and Instruments in Chemical Industry, vol. 39, no. 9, pp. 1111–1114, 2012.
  • Z. Xiaomu and Z. Mingjun, “Optimum configuration in pressure regulation system at delivery station in gas pipeline,” Natural Gas and Oil, vol. 23, no. 4, pp. 27–29, 2005.
  • Z. Rongrong, Z. Shiwen, and S. Guohua, “The design of an intelligent controller for use with electric valve-actuators,” Industral Instrumentation Automation, no. 4, pp. 26–28, 2005.
  • L. Zhiguo, L. Jian, and X. Lixin, “Electric pressure regulating valve and self-reliance type pressure regulating valve of combination station,” Oil-Gas Field Surface Engineering, vol. 31, no. 11, p. 108, 2012.
  • L. Wang, W. F. Li, and D. Z. Zheng, “Optimal design of controllers for non-minimum phase systems,” Acta Automatica Sinica, vol. 29, no. 1, pp. 135–141, 2003.
  • Y. D. Song, Q. Cao, X. Du, and H. R. Karimi, “Control strategy based on wavelet transform and neural network for hybrid power system,” Journal of Applied Mathematics, vol. 2013, Article ID 375840, 8 pages, 2013.
  • A. Zhang, C. Chen, and H. R. Karimi, “A new adaptive LSSVR with online multikernel RBF tuning to evaluate analog circuit performance,” Abstract and Applied Analysis, vol. 2013, Article ID 231735, 7 pages, 2013.
  • X. Dong, Y. Zhao, H. R. Karimi, and P. Shi, “Adaptive variable structure fuzzy neural identification and control for a class of MIMO nonlinear system,” Journal of the Franklin Institute, vol. 350, no. 5, pp. 1221–1247, 2013.
  • C. C. Chuang, J. T. Jeng, and P. T. Lin, “Annealing robust radial basis function networks for function approximation with outliers,” Neurocomputing, vol. 56, no. 1–4, pp. 123–139, 2004.
  • J. K. Sing, D. K. Basu, M. Nasipuri, and M. Kundu, “Face recognition using point symmetry distance-based RBF network,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 58–70, 2007.
  • H. Liu, S. Wang, and P. Ouyang, “Fault diagnosis in a hydraulic position servo system using RBF neural network,” Chinese Journal of Aeronautics, vol. 19, no. 4, pp. 346–353, 2006.
  • Z. A. Bashir and M. E. El-Hawary, “Applying wavelets to short-term load forecasting using PSO-based neural networks,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 20–27, 2009.
  • C. Zhang, M. Lin, and M. Tang, “BP neural network optimized with PSO algorithm for daily load forecasting,” in Proceedings of the International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII '08), vol. 56, pp. 82–85, December 2008.
  • K. Meng, Z. Y. Dong, D. H. Wang, and K. P. Wong, “A self-adaptive RBF neural network classifier for transformer fault analysis,” IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1350–1360, 2010.
  • G. Li, Q. Zhang, and Y. Liang, “GA-based PID neural network control for magnetic bearing systems,” Chinese Journal of Mechanical Engineering, vol. 20, no. 2, pp. 56–59, 2007.
  • W. Lianghong, W. Yaonan, Z. Shaowu, and T. Wen, “Design of pid controller with incomplete derivation based on differential evolution algorithm,” Journal of Systems Engineering and Electronics, vol. 19, no. 3, pp. 578–583, 2008.
  • J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995.
  • M. Barari, H. R. Karimi, and F. Razaghian, “Analog circuit design optimization based on evolutionary algorithms,” Mathematical Problems in Engineering, vol. 2014, Article ID 593684, 12 pages, 2014.
  • M. Sheikhan, R. Shahnazi, and S. Garoucy, “Synchronization of general chaotic systems using neural controllers with application to secure communication,” Neural Computing and Applications, vol. 22, no. 2, pp. 361–373, 2013.
  • R. Mooney, “Pilot-loaded regulators: what you need to know,” Gas Industries, vol. 34, no. 1, pp. 31–33, 1989.
  • A. Krigman, Guide to Selecting Pressure Regulators, InTech, 1984.
  • A. E. Rundell, S. V. Drakunov, and R. A. DeCarlo, “A sliding mode observer and controller for stabilization of rotational motion of a vertical shaft magnetic bearing,” IEEE Transactions on Control Systems Technology, vol. 4, no. 5, pp. 598–608, 1996.
  • N. Zafer and G. R. Luecke, “Stability of gas pressure regulators,” Applied Mathematical Modelling, vol. 32, no. 1, pp. 61–82, 2008.
  • Y. Zhong, F. Wang, L. Zhang, and C. Tao, “The information fusion algorithm on gas čommentComment on ref. [26?]: Please provide all the names of the authors for this reference.pressure regulator inlet/outlet detection,” Journal of Computational Information Systems, vol. 10, no. 5, pp. 2145–2153, 2014.
  • W. Zhi, Study on Dynamic Characteristic and Control of Electric Control Valve, Shan Dong University, 2013.
  • Y. H. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the Congress on Evolutionary Computation, pp. 1945–1950, Piscataway, NJ, USA, 1999. \endinput