Review of Method for System Identification on Motors

The industry is closely related to motors. Motor is used as the prime mover to run the production machines. Control of the motor is needed so that it can work according to its designation. Motor parameters must be known to control it. The required parameters include electrical and mechanical parameters. These parameters are often not easy to obtain then one way to find out is by identifying the system. This paper aimed to convey the various methods that have been used in motor identification systems. Brushed DC motor, brushless DC motor, servo motor, stepper motor, induction motor, and switch reluctance motor were motors analyzed. These methods included the least square, recursive least square in the form of autoregressive with exogenous input, autoregressive moving average with exogenous. Another system identification method utilizes artificial intelligence. This method used fuzzy logic, neural network, genetic algorithm, particle swarm optimization, and various combinations of these methods. The review results showed that the artificial intelligence method was very interesting and promising because it has advantages compared to conventional methods. Modification or combination of two or more artificial intelligence methods would get better and closer results to the actual situation.