Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

DEVELOPMENT OF A RECONFIGURABLE CONTROL FOR AN SP-100 SPACE REACTOR

  • Na Man-Gyun (Department of Nuclear Engineering, Chosun University) ;
  • Upadhyaya Belle R. (Department of Nuclear Engineering, The University of Tennessee)
  • Published : 2007.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a reconfigurable controller consisting of a normal controller and a standby controller is designed to control the thermoelectric (TE) power in the SP-100 space reactor. The normal controller uses a model predictive control (MPC) method where the future TE power is predicted by using support vector regression. A genetic algorithm that can effectively accomplish multiple objectives is used to optimize the normal controller. The performance of the normal controller depends on the capability of predicting the future TE power. Therefore, if the prediction performance is degraded, the proportional-integral (PI) controller of the standby controller begins to work instead of the normal controller. Performance deterioration is detected by a sequential probability ratio test (SPRT). A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed reconfigurable controller. The results of numerical simulations to assess the performance of the proposed controller show that the TE generator power level controlled by the proposed reconfigurable controller could track the target power level effectively, satisfying all control constraints. Furthermore, the normal controller is automatically switched to the standby controller when the performance of the normal controller degrades.

Keywords

References

  1. S. F. Demuth, 'SP-100 Space Reactor Design,' Progress in Nuclear Energy, Vol. 42, No. 3, pp. 323-359, 2003 https://doi.org/10.1016/S0149-1970(03)90003-5
  2. Y. B. Shtessel, 'Sliding Mode Control of the Space Nuclear Reactor System,' IEEE Trans. Aerospace and Electronic Systems, Vol. 34, No. 2, pp. 579-589, 1998 https://doi.org/10.1109/7.670338
  3. M. G. Na and B. R. Upadhyaya, 'Model Predictive Control of an SP-100 Space Reactor Using Support Vector Regression and Genetic Optimization,' IEEE Trans. Nucl. Sci., Vol. 53, No. 4, pp. 2318-2327, Aug. 2006 https://doi.org/10.1109/TNS.2006.876517
  4. M. G. Na, B. R. Upadhyaya, X. Xu and I. J. Hwang, 'Design of a Model Predictive Power Controller for an SP-100 Space Reactor,' Nucl. Sci. Eng., Vol. 154, No.3, pp. 353-366, Nov. 2006 https://doi.org/10.13182/NSE06-A2638
  5. W. H. Kwon and A. E. Pearson, 'A Modified Quadratic Cost Problem and Feedback Stabilization of a Linear System,' IEEE Trans. Automatic Control, Vol. 22, No. 5, pp. 838- 842, 1977 https://doi.org/10.1109/TAC.1977.1101619
  6. J. Richalet, A. Rault, J. L. Testud and J. Papon, 'Model Predictive Heuristic Control: Applications to Industrial Processes,' Automatica, Vol. 14, pp. 413-428, 1978 https://doi.org/10.1016/0005-1098(78)90001-8
  7. C. E. Garcia, D. M. Prett and M. Morari, 'Model Predictive Control: Theory and Practice – A Survey,' Automatica, Vol. 25, No. 3, pp. 335-348, 1989 https://doi.org/10.1016/0005-1098(89)90002-2
  8. D. W. Clarke and R. Scattolini, 'Constrained Receding- Horizon Predictive Control,' IEE Proceedings-D, Vol. 138, No. 4, pp. 347-354, 1991
  9. M. V. Kothare, V. Balakrishnan and M. Morari, 'Robust Constrained Model Predictive Control Using Linear Matrix Inequality,' Automatica, Vol. 32, No. 10, pp. 1361-1379, 1996 https://doi.org/10.1016/0005-1098(96)00063-5
  10. J. W. Lee, W. H. Kwon and J. H. Lee, 'Receding Horizon $H^{\infty}$ Tracking Control for Time-Varying Discrete Linear Systems,' Intl. J. Control, Vol. 68, No. 2, pp. 385-399, 1997 https://doi.org/10.1080/002071797223686
  11. J. W. Lee, W. H. Kwon and J. Choi, 'On Stability of Constrained Receding Horizon Control with Finite Terminal Weighting Matrix,' Automatica, Vol. 34, No. 12, pp. 1607- 1613, 1998 https://doi.org/10.1016/S0005-1098(98)80015-0
  12. M. G. Na, S. H. Shin and W. C. Kim, 'A model predictive controller for nuclear reactor power,' J. Korean Nucl. Soc., Vol. 35, No. 5, pp. 399-411, Oct. 2003
  13. P.-F. Pai and W.-C. Hong, 'Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting,' Energy Conversion and Management, Vol. 46, pp. 2669-2688, 2005 https://doi.org/10.1016/j.enconman.2005.02.004
  14. W. Yan, H. Shao and X. Wang, 'Soft Sensing Modeling Based on Support Vector Machine and Bayesian Model Selection,' Computers and Chemical Engineering, Vol. 28, pp. 1489-1498, 2004 https://doi.org/10.1016/j.compchemeng.2003.11.004
  15. A. Kulkarni, V. K. Jayaraman and B. D. Kulkarni, 'Control of Chaotic Dynamical Systems Using Support Vector Machines,' Physics Letters A, Vol. 317, pp. 429-435, 2003 https://doi.org/10.1016/j.physleta.2003.09.004
  16. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995
  17. H.W. Kuhn and A.W. Tucker, 'Nonlinear programming,' Proc. 2nd Berkeley Symp. Math. Stat. Probab., Univ. California Press, pp. 481–492, 1951
  18. H. Sarimveis and G. Bafas, 'Fuzzy Model Predictive Control of Non-Linear Processes Using Genetic Algorithms,' Fuzzy Sets Systems, Vol. 139, pp. 59-80, 2003 https://doi.org/10.1016/S0165-0114(02)00506-7
  19. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, Reading, MA, 1989
  20. M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA, 1996
  21. M. G. Na and I. J. Hwang, 'Design of a PWR Power Controller Using Model Predictive Control Optimized by a Genetic Algorithm,' Nucl. Eng. Tech., Vol. 38, No. 1, pp. 81-92, Feb. 2006
  22. A. Wald, Sequential Analysis, John Wiley & Sons, New York, 1947
  23. B. R. Upadhyaya, F. P. Wolvaardt and O. Glockler, 'An Integrated Approach for Signal Validation in Dynamic Systems,' Progress in Nuclear Energy, Pergamon Press, Vol. 21, pp. 605-611, 1988 https://doi.org/10.1016/0149-1970(88)90082-0
  24. B. R. Upadhyaya and K. Zhao, 'Dynamic Modeling and Control of Space Nuclear Power Systems,' Proc. Space Nuclear Conference 2005, San Diego, California, June 5-9, 2005
  25. J. T. Seo, 'SNPSAM-Space Nuclear Power System Analysis Model,' M.S. thesis, University of New Mexico, 1986
  26. B.R. Upadhyaya, K. Zhao and X. Xu, Autonomous Control of Space Reactor Systems, Annual Report prepared for the U.S. Department of Energy NEER Program, Report No. DE-FG07-04ID14589/UTNE-02 (2005)
  27. MathWorks, MATLAB 5.3 (Release 11). Natick, MA: The MathWorks, 1999