PID Controller Design Based on Radial Basis Function Neural Networks for the Steam Generator Level Control

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Steam generator level control system is a vital control system for the Pressurized Water Reactor (PWR). However, the steam generator level process is a highly nonlinear and non-minimum phase system, the conventional Proportional- Integral-Derivative (PID) control scheme with fixed parameters was difficult to obtain satisfactory control performance. The Radial Basis Function (RBF) Neural Networks based PID control strategy (RBFNN-PID) is proposed for the steam generator level control. This method can identify the mathematical model of the steam generator via the RBF neural networks, and then the PID parameters can be optimized automatically to accommodate the characteristic variation of the process. The optimal number of the hidden layer neurons is also discussed in this paper. The simulation results shows that the PID controller designed based on the RBF neural networks has good control performance on the steam generator level control.

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  • 1. Mayuresh V. K. Level Control in the Steam Generator of a Nuclear Power Plant. - IEEE Transactions on Control Systems Technology Vol. 8 2000 No 1 pp. 55-69.

  • 2. Ablay G. Robust Estimator-Based Optimal Control Designs for U-Tube Steam Generators. - Transactions of the Institute of Measurement and Control Vol. 37 2015 No 5 pp. 636-644.

  • 3. Fang F. Y. Xiong. Event-Driven-Based Water Level Control for Nuclear Steam Generators. - IEEE Transactions on Industrial Electronics Vol. 61 2014 No 10 pp. 5480-5489.

  • 4. Irving E. C. Miossec J. Tassart. Toward Efficient Full Automatic Operation of the PWR Steam Generator with Water Level Adaptive Control. - In: Proc. of 2nd Int. Conf. Boiler Dynamics and Control in Nuclear Power Stations Bournemouth U.K. October 1979 pp. 309-329.

  • 5. Feliachi A. L. A. Belbelidia. Suboptimal Level Controller for Steam Generators in Pressurized Water Reactors. - IEEE Trans. Energy Convers. Vol. 3 1988 pp. 278-284.

  • 6. Parlos A. G. O. T. Rais. Nonlinear Control of U-Tube Steam Generators via H∞ Control. - Control Eng. Pract. Vol. 8 2000 pp. 921-936.

  • 7. Na M. G. Auto-Tuned PID Controller Using a Model Predictive Control Method for the Steam Generator Water Level. - IEEE Trans. Nucl. Sci. Vol. 48 2001 pp. 1664-1671.

  • 8. Ansarifar G. R. Control of the Nuclear Steam Generators Using Adaptive Dynamic Sliding Mode Method Based on the Nonlinear Model. - Annals of Nuclear Energy Vol. 88 2016 pp. 280-300.

  • 9. Na M. G. Design of Genetic Fuzzy Controller for the Nuclear Steam Generator Water Level Control. - IEEE Trans. Nucl. Sci. Vol. 45 1998 pp. 2261-2271.

  • 10. Åström K. J. T. Hägglund. PID Controllers: Theory Design and Tuning. 2nd Ed. Research Triangle Park NC: Instrum. Soc. Amer. 1995.

  • 11. Haykin S. Neural Networks: A Comprehensive Foundation. 3rd Ed. NJ USA Prentice-Hall Inc. 2007.

  • 12. Wang J.-J. C.-F. Zhang Y.-Y. Jing. Self-Adaptive RBF Neural Network PID Control in Exhaust Temperature of Micro Gas Turbine. - In: Proc. of 7th International Conference on Machine Learning and Cybernetics Vol. 4 July 2008 Kunming China pp. 12-15.

  • 13. Elanayar S. V. T. Y. C. Shin. Radial Basis Unction Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic Systems. - IEEE Transaction on Neural Network Vol. 5 1994 No 4 pp. 584-603.

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