Design of low cost universal artificial neuron controller for chopper fed embedded DC drives

  • Authors:

  • N. Senthil Kumar;V. Sadasivam;H. M. Asan Sukriya;S. Balakrishnan

  • Affiliations:

  • EEE Department, Mepco Schlenk Engineering College, Sivakasi, Virudhunagar 626005, Tamilnadu, India;Computer Science and Engineering Department, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India;EEE Department, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India;Mepco Schlenk Engineering College, Sivakasi, Virudhunagar 626005, Tamilnadu, India

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2008

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Abstract

Artificial neural network (ANN) has become very popular in many control applications due to their high computation rate and ability to handle nonlinear functions. This paper proposes an artificial neuron controller for closed loop speed control of DC drive fed by DC chopper. Neuron control is used to reduce the steady state error, overshoot and settling time. The signal corresponding to the motor speed error and change in speed error are used as inputs to ANN Controller. The controller outputs the required change in duty cycle of pulse width modulated gating signal applied to DC chopper. Thus the voltage fed to the armature of the DC motor is adjusted for achieving the desired speed response. The training patterns for the neuron controller are obtained from the conventional PI controller and the effectiveness of the proposed neuron controller is studied using simulation studies. The designed controller was implemented in a low cost 8051-based embedded system and the results are documented. Two-loop control system was implemented with an inner ON/OFF current controller and an outer ANN speed controller. A conventional controller has heavy computation burden whereas a trained neural network requires less computation time. The artificial neural network has the ability to generalize and can interpolate in between the training data. This advantage of ANN makes the ANN controller universal. The ANN controller designed was tested on two different motors and found to work effectively on driving both of them.