6533b82cfe1ef96bd128f5bf

RESEARCH PRODUCT

An adaptive prudent-daring evolutionary algorithm for noise handling in on-line PMSM drive design

S. StasiGiuseppe Leonardo CascellaN. SalvaloreFerrante Neri

subject

NoiseControl theoryComputer scienceEvolutionary algorithmOptimal controlEvolutionary computationSelection (genetic algorithm)

description

This paper studies the problem of the optimal control design of permanent magnet synchronous motor (PMSM) drives taking into account the noise due to sensors and measurement devices. The problem is analyzed by means of an experimental approach which considers noisy data returned by the real plant (on-line). In other words, each fitness evaluation does not come from a computer but from a real laboratory experiment. In order to perform the optimization notwithstanding presence of the noise, this paper proposes an Adaptive Prudent- Daring Evolutionary Algorithm (APDEA). The APDEA is an evolutionary algorithm with a dynamic parameter setting. Furthermore, the APDEA employs a dynamic penalty term and two cooperative-competitive survivor selection schemes. The numerical results show that the APDEA robustly executes optimization in the noisy environment. In addition, comparison with other meta-heuristics shows that behavior of the APDEA is very satisfactory in terms of convergence velocity. A statistical test confirms the effectiveness of the APDEA.

yearjournalcountryeditionlanguage
2007-09-012007 IEEE Congress on Evolutionary Computation
https://doi.org/10.1109/cec.2007.4424523