System Identification Based Data Driven Control Of DC Motor Using NARX Neural Network

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M. Panneerselvam, Prakash Raja Anthony, S. Priyadharshan, P. M. Raghavendra

Abstract

DC motors are mostly used in industries. In DC motor system should be produce some changes in the unpredictable, load dynamics, variable and disturbances, unknown parameters. It will result in motor performances. Therefore the motor should be controlled and analysed. Without the plant model, the design of controller for the DC motor is very difficult. Using Data driven control method, it requires only the measured input data and measured output data of the plant, the system identification model and controller design. The designing of mathematical model of the DC motor by using system identification technique is based on perceived system data. By utilizing simscape Simulink, DC motor model is being constructed. The measured input data as (voltage) and output data as (speed) of the DC motor are used to execute system identification process. The results of the system identification process are to be compared with the performances of NARX neural network architecture. The Simulink model for the DC motor is developed and implementation of data driven control system of the DC motor using system identification.

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