Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies

Marek Lis¹  Andriy Chaban²  Radoslaw Jedynak²  Andrzej Szafraniec² 

[1] Faculty of Electrical Engineering, Czestochowa University of Technology, Al. Armii Krajowej 17, 42-201 Czestochowa, Poland;Faculty of Transport, Electrical Engineering and Computer Science, Kazimierz Pulaski University of Technology and Humanities, Malczewskiego 29, 26-600 Radom, Poland;

关键词: mathematical modelling;    genetic algorithm;    long shaft equations;    Hamilton–Ostrogradski principle;    analytical mechanics;    computer simulation;   

DOI  :  10.3390/en14010115

来源: DOAJ

【 摘 要 】

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

【 授权许可】

Unknown