Prudent-Daring vs Tolerant Survivor Selection Schemes in Control Design of Electric Drives
This paper proposes and compares two approaches to defeat the noise due the measurement errors in control system design of electric drives. The former is based on a penalized fitness and two cooperative-competitive survivor selection schemes, the latter is based on a survivor selection scheme which makes use of the tolerance interval related to the noise distribution. These approaches use adaptive rules in parameter setting to execute both the explicit and the implicit averaging in order to obtain the noise defeating in the optimization process with a relatively low number of fitness evaluations. The results show that the two approaches differently bias the population diversity and that the first can outperform the second but requires a more accurate parameter setting.
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- 1.Branke, J.: Evolutionary Optimization in Dynamic Enviroments. Kluwer, Dordrecht (2001)Google Scholar
- 3.Krishnan, R.: Electronic Motor Drives: Modeling, Analysis and Control. Prentice-Hall, Upper Saddle River (2001)Google Scholar
- 4.Leonhard, W.: Control of Electrical Drives, 2nd edn. Springer, Heidelberg (1996)Google Scholar
- 5.Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of pmsm drives. To app. IEEE Trans. on System Man and Cybernetics-B, spec. issue Memetic Alg.s (2006)Google Scholar
- 6.Whitley, D.: The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In: Proc. 3rd Int. Conf. on GAs, pp. 116–121 (1989)Google Scholar
- 7.Eshelman, L.J., Shaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms, vol. 2, pp. 187–202. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 9.NIST: e-handbook of statistical methods, http://www.itl.nist.gov/div898/handbook/