The Research On Optimal Design Of Brushless Direct Current Motor Base On JAYA Algorithm

Brushless direct current(BLDC)motor has been used in industrial production and engineering applications due to its simple structure,wide speed range and low noise.In order to raise the operation efficiency of the BLDC motor and reduce the production and application cost of the motor,the research on the optimization design method of the BLDC motor has always been a hot issue in the field of motor production and design.However,the design of BLDC motor is a kind of typical complex high-dimensional,multi-constraint,nonlinear optimization problem.Therefore,how to design an efficient optimization method for key parameters of BLDC motor is a difficult and key problem in this field.In this paper,the optimization design method of BLDC motor is deeply explored and researched in detail.Starting from the analysis and construction of the mathematical model of BLDC motor,the advantages and disadvantages of traditional optimization methods and common intelligent optimization algorithms are analyzed and summarized,and the goal is to improve the output efficiency of BLDC motor.An optimal design method of BLDC motor based on JAYA algorithm is proposed.The main research contents are as follows:Simultaneously,the optimization design of motor is a kind of complex nonlinear optimization problem,so it is very important to study the optimization design method of BLDC motor.Therefore,this thesis carries out the detail discussion and research about the optimization design method of BLDC motor.By analyzing and studying the features of the traditional optimization method,this paper will offer a study of an optimization research of the BLDC motor based on a newly-designed JAYA algorithm according to the characteristics of the optimization design of the motor.The major research content of this paper can be separated into the following aspects:First of all,the research background and significance of the optimization design of BLDC motor are described,and the research status of the optimization design methods of BLDC motor is summarized,and the advantages and disadvantages of traditional optimization methods and common intelligent optimization algorithms are analyzed and summarized in detail.In addition,the fundamental structure,operating theory and efficiency calculation process of the BLDC motor are introduced.Based on the above theories,the mathematical model of parameters optimization of the BLDC motor is established.Then,in order to avoid complex algorithm parameter setting and improve the optimization performance of BLDC motor,a modified JAYA algorithm(MJAYA)is proposed.An empirical learning strategy is introduced to MJAYA algorithm to improve the population diversity and optimization performance of the algorithm.Compared with other algorithms,the proposed algorithm is used to solve the optimization design problem of BLDC motor and benchmark test function,so as to test the effectiveness and optimization performance of the proposed algorithm.Finally,a self-adaptive JAYA algorithm(SAJAYA)was proposed to solve the problems of slow convergence speed and poor quality of optimal solution of MJAYA algorithm in the optimization of BLDC motor.The algorithm designs a hybrid learning strategy to maintain the diversity of the population.On this basis,an adaptive selection mechanism based on individual evolutionary state is proposed to balance the exploration and exploitation abilities of the algorithm.In addition,an adaptive weight strategy is introduced to the original JAYA to control the degree of the individuals approaching the best solution and avoiding the worst solution during the different evolution stages.Finally,a chaotic elite search strategy is introduced to refine the quality of the optimal solution.Taking antitheses the method,variants of the proposed algorithm are compared to confirm the effectiveness of the strategy put forward.Simultaneously,SAJAYA algorithm is compared with some common algorithms to solve the optimization design problem of BLDC motor and test benchmark function.Experimental results show that the proposed algorithm has certain advantages compared with other algorithm.