Control parameter optimal tuning method based on annealing-genetic algorithm for complex electromechanical system

Abstract

A new searching algorithm named the annealing-genetic algorithm (AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA, and offsets their shortcomings. The difference from GA is that AGA takes objective function as adaptability function directly, so it cuts down some unnecessary time expense because of float-point calculation of function conversion. The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space, so it is simple and easy to be implemented. It can be applied to a wide class of problems. The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA. The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.

Keywords

genetic algorithm / simulated annealing algorithm / annealing-genetic algorithm / complex electromechanical system / parameter tuning / optimal control

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