Experimental verification of chopper fed DC series motor with ANN controller

Abstract

In this article an artificial neural network (ANN) has been designed for the control of DC series motor through a DC chopper (DC-DC buck converter). The proportional-integral-derivative (PID)-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady-state and dynamic operating condition with various set speeds and various load torques. The rise time, maximum overshoot, settling time, steady-state error, and speed drops are taken for comparison with conventional PID controller and existing work. The training samples for the neuron controller are acquired from the conventional PID controller. The PID-ANN controller performances are analyzed in respect of various load torques and various speeds using MATLAB simulation. Then the designed controllers were experimentally verified using an NXP 80C51 based microcontroller (P89V51RD2BN). It was found that the hybrid PID-ANN controller with DC chopper can have better control compared with conventional PID controller.

Key words： DC series motor; proportional-integral-derivative (PID) controller; artificial neural network (ANN) controller; DC chopper; speed control; MATLAB simulink

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