Determination of the Weighting Parameters of the LQR System for Nuclear Reactor Power Control Using the Stochastic Searching Methods

  • Published : 1997.02.01

Abstract

The reactor power control system is described in the fashion of the order increased LQR system. To obtain the optimal state feedback gain vectors, the weighting matrix of the performance function should be determined. Since the contentional method has some limitations, stochastic searching methods are investigated to optimize the LQR weighting matrix using the modified genetic algorithm combined with the simulated annealing, a new optimizing tool named the hybrid MGA-SA is developed to determine the weighting parameters of the LQR system. This optimizing tool provides a more systematic approach in designing the LQR system. Since it can be easily incorporated with any forms of the cost function, it also provides the great flexibility in the optimization problems.

Keywords

References

  1. J. of the KNS v.26 no.4 The Control Rod Speed Design for the Nuclear Reactor Power Control Using Optimal Control Theory Y.J. Lee
  2. Procd. of ANS Topical Meeting on NPIC A Conceptual Design of the Digital Nuclear Power Control System by the Order Increased LQR Method Y.J. Lee
  3. Science v.220 Optimization by Simulated Annealing S. Kirkpatrick;C.D. Gelatt Jr.;M.P. Vecchi
  4. Simulated Annealing: Theory and Applications P.J.M. van Laarhoven;E.H.L. Aarts
  5. Simulated Annealing for VLSI Design D.F. Wong;H.W. Leong;C.L. Liu
  6. J. of KSME v.17 no.5 Tool Path Optimization for NC Turret Operation Using Simulated Annealing K.H. Cho;K. Lee
  7. J. of KSME v.19 no.8 An Effective Method for the Nesting on Several Irregular Raw Sheets K.H. Cho;K. Lee
  8. Proc Instn Mech Engrs, Part B : J. of Engineering Manufacture v.209 Optimization of Tool/Workpiece Orientation in Designing Die-Face of Automobile Outer Panels J.C. Park;K. Lee;K.H. Cho
  9. Genetic Algorithm in Search, Optimization & Machine Learning D.E. Goldberg
  10. Genetic Programming on the Programming of Computers by Means of Natural Selection J.R. Koza
  11. Machine Learning v.3 Learning with Genetic Algorithms: An Overview K. DeJong
  12. Genetic Algorithms B.P. Buckles;F.E. Petry
  13. Computer v.27 no.6 Genetic Algorithms : A Survey M. Srinivas;L.M. Patnaik
  14. Computer v.27 no.6 Genetic-Algorithm Programming Environments L. Jose;C. Alippi
  15. Numerical Methods J.D. Faires;R.L. Burden
  16. J. of KSEE v.33 no.B-4 A Proposal of New Method for EICT Image Reconstruction-A Hybrid Approach Using Genetic Algorithm and Newton-Raphson Method- K.H. Cho;S.T. Ko;H.S. Ko
  17. J. of the Korean Physical Society v.29 no.4 Determination of the Optimal Parameters for the Meson Spectra Analysis Using the Hybrid Genetic Algorithm and Newton Method K.H. Cho;N.G. Hyun;J.B. Choi