Surrogate assisted local search in PMSM drive design

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering

Volumn 27, Issue 3, 2008, Pages 573-592

  Neri, Ferrante ^a   Del Toro Garcia, Xavier ^b   Cascella, Giuseppe L ^c   Salvatore, Nadia ^c  

^a UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^b UNIVERSITY OF GLAMORGAN   (United Kingdom)

^c POLITECNICO DI BARI   (Italy)

Author keywords

Magnetic devices; Numerical analysis; Optimization techniques

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; ELECTRIC DRIVES; NUMERICAL ANALYSIS; PERMANENT MAGNETS; PROBLEM SOLVING; SYNCHRONOUS MOTORS;

COMPUTATIONAL COST; LOCAL SEARCH ALGORITHM; LOCAL SURROGATE MODEL; OPTIMIZATION TECHNIQUES;

MAGNETIC DEVICES;

EID: 43049116888     PISSN: 03321649     EISSN: None     Source Type: Journal    
DOI: 10.1108/03321640810861043     Document Type: Article

References (28)

1. Branke, J. (2001), Evolutionary Optimization in Dynamic Environments, Kluwer A.P, Dordrecht, pp. 125-72.
2. Branke, J., Schmidt, C. and Schmeck, H. (2001), “Efficient fitness estimation in noisy environment”, in Spector, L. et al. (Eds), Genetic and Evolutionary Computation, Morgan Kauffman, San Francisco, CA, pp. 243-55.
3. Canova, A., Freschi, F. and Repetto, M. (2004), “Hybrid method coupling AIS and zeroth order deterministic search”, COMPEL: International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 23 No. 3, pp. 784-95.
4. Caponio, A., Cascella, G.L., Neri, F., Salvatore, N. and Sumner, M. (2007), “A fast adaptive memetic algorithm for on-line and off-line control design of PMSM drives”, IEEE Transactions On Systems, Man and Cybernetics – Part B, Vol. 37, special issue on Memetic Algorithms, pp. 28-41.
5. Cupertino, F., Giordano, V., Naso, D., Turchiano, B. and Salvatore, L. (2003), “On-line genetic design of fuzzy controllers for DC drives with variable load”, Electronics Letters, Vol. 39 No. 3, pp. 479-80.
6. Cupertino, F., Mininno, E., Naso, D., Turchiano, B. and Salvatore, L. (2004), “On-line genetic design of anti-windup unstructured controllers for electric drives with variable load”, IEEE Transaction on Evolutionary Computation, Vol. 8 No. 4, pp. 347-64.
7. da Silva, W.G., Acarnley, P.P. and Finch, J.W. (2000), “Application of genetic algorithms to the online tuning of electric drive speed controllers”, IEEE Transactions on Industrial Electronics, Vol. 47 No. 1, pp. 217-9.
8. Delvecchio, G., Lofrumento, C., Neri, F. and Sylos Labini, M. (2006), “A fast evolutionary-deterministic algorithm to study multimodal current fields under safety level constraints”, COMPEL: International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 25 No. 3, pp. 599-608.
9. DS1103 (2003), DS1103 PPC Controller Board Hardware Reference, dSPACE GmbH, Paderborne.
10. Eiben, A.E. and Smith, J.E. (2003), Introduction to Evolutionary Computation, Springer, Berlin.
11. Gross, H., Hamann, J. and Wiegärtner, G. (2001), Electrical Feed Drives in Automation: Basics, Computation, Dimensioning, Siemens Aktiengesellschaft, Munich.
12. Hart, W.E., Krasnogor, N. and Smith, J.E. (2004), “Memetic evolutionary algorithms”, in Hart, W.E., Krasnogor, N. and Smith, J.E. (Eds), Recent Advances in Memetic Algorithms, Springer, Berlin, pp. 3-27.
13. Hooke, R. and Jeeves, T.A. (1961), “Direct search solution of numerical and statistical problems”, Journal of the ACM, Vol. 8, pp. 212-29.
14. Kaupe, F. Jr (1963), “Algorithm 178: direct search”, Communications of the ACM, Vol. 6 No. 6, pp. 313-4.
15. Kelley, C.T. (1999), Iterative Methods of Optimization, SIAM, Philadelphia, PA, pp. 212-29.
16. Krasnogor, N. (2004), “Toward robust memetic algorithms”, in Hart, W.E., Krasnogor, N. and Smith, J.E. (Eds), Recent Advances in Memetic Algorithms, Springer, Berlin, pp. 185-207.
17. Krishnan, R. (2001), Electric Motor Drives: Modeling, Analysis and Control, Prentice-Hall, Upper Saddle River, NJ.
18. Leonhard, W. (1996), Control of Electrical Drives, 2nd ed., Springer, Berlin.
19. Miettinen, K. (1998), Nonlinear Multiobjective Optimization, Springer, Berlin.
20. Nelder, A. and Mead, R. (1965), “A simplex method for function optimization”, Computation Journal, Vol. 7, pp. 308-13.
21. Neri, F., Cascella, G.L., Salvatore, N., Kononova, A.V. and Acciani, G. (2006), “Prudent-daring vs tolerant survivor selection schemes in control design of electric drives”, in Rothlauf, F. et al. (Eds), Applications of Evolutionary Computing, Lecture Notes in Computer Science, Vol. 3907, Springer, Berlin, pp. 805-9.
22. Ong, Y.S., Nair, P.B. and Lum, K.Y. (2006), “Max-min surrogate-assisted evolutionary algorithm for robust design”, IEEE Tansactions on Evolutionary Computationm, Vol. 10 No. 4, pp. 392-404.
23. Ong, Y.S., Nair, P. and Keane, A. (2003), “Evolutionary optimization of computationally expensive problems via surrogate modeling”, American Institute of Aeronautics and Astronautics Journal, Vol. 41 No. 4, pp. 687-96.
24. Ong, Y.S., Nair, P.B., Keane, A.J. and Wong, K.W. (2004), “Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems”, in Jin, Y. (Ed.), Knowledge Incorporation in Evolutionary Computation, Springer, Berlin, pp. 307-31.
25. Pahner, U., Belmans, R. and Hameyer, K. (1999), “Optimizing ferromagnetic characteristics to reduce Newton iterations”, COMPEL: International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 18 No. 4, pp. 629-37.
26. Panagopoulos, H., Åström, K.J. and Häagglund, T. (1999), “Design of PID controllers based on constrained optimization”, Proceedings of 1999 American Control Conference (ACC'99). San Diego, CA, USA, pp. 3858-62.
27. Sano, Y., Kita, H., Kamihira, I. and Yamaguchi, M. (2000), “Online optimization of an engine controller by means of a genetic algorithm using history of search”, Simulated Evolution and Learning, Springer, Berlin, pp. 2929-34.
28. Zhou, Z.Z., Ong, Y.S., Nair, P.B., Keane, A.J. and Lum, K.Y. (2007), “Combining global and local surrogate models to accelerate evolutionary optimization”, IEEE Tansactions on System Man and Cybernetics – Part C., Vol. 37 No. 1, pp. 66-76.