Adaptive Real-Coded Genetic Algorithm for Identifying Motor Systems  

作　　者：Rong-Fong Fung Chun-Hung Lin 

机构地区：[1]Department of Mechanical & Automation Engineering, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan [2]Graduate Institute of Electrical Engineering, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan

出　　处：《Modern Mechanical Engineering》2015年第3期69-86,共18页现代机械工程（英文）

摘　　要：In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.

关 键 词：ADAPTIVE Real-Coded GENETIC Algorithm (ARGA) BRUSHLESS Direct Current MOTOR (BLDC) Electrical FAN Induction MOTOR System Identification 

分 类 号：R73[医药卫生—肿瘤]