In this paper, the main objective is to identify the parameters of motors, which includes a brushless\ndirect current (BLDC) motor and an induction motor. The motor systems are dynamically\nformulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is\nadopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various\nadaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness\nvalues, which are the inverse of square differences of angular speeds. From numerical simulations\nand experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively\nsolve the problems with slow convergent speed and premature phenomenon, and is more\naccurate in identifying system�s parameters than the SRGA. From the comparisons of the ARGAs in\nidentifying parameters of motors, the best ARGA method is obtained and could be applied to any\nother mechatronic systems.
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