This paper describes the implementation of self-optimizing embedded control schemes for induction motor drives. The online design problem is formulated as a search problem and solved with stochastic optimization algorithms. The objective function takes into account the tracking error, and is directly measured on the hardware bench. In particular, we compare two efficient optimization algorithms, a simultaneous perturbation stochastic approximation method, and a compact genetic algorithm. Both search strategies have very small computational requirements, and therefore can be directly implemented on the same processor running the control algorithm.

A comparison of SPSA method and compact genetic algorithms for the optimization of induction motor position control

F. Cupertino;Mininno, Ernesto;D. Naso;Salvatore, Luigi
2007-01-01

Abstract

This paper describes the implementation of self-optimizing embedded control schemes for induction motor drives. The online design problem is formulated as a search problem and solved with stochastic optimization algorithms. The objective function takes into account the tracking error, and is directly measured on the hardware bench. In particular, we compare two efficient optimization algorithms, a simultaneous perturbation stochastic approximation method, and a compact genetic algorithm. Both search strategies have very small computational requirements, and therefore can be directly implemented on the same processor running the control algorithm.
2007
European Conference on Power Electronics and Applications, EPE 2007
978-90-75815-11-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/15611
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