Speed control of separately excited DC motor

American Journal of Applied Sciences, March, 2008 by Moleykutty George

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In the system identification stage a neural network plant model must be developed before the controller is used. The plant model predicts future plant outputs. The plant model has only one hidden layer. The specifications of the plant model are tabulated in Table 1.

Table 1: Plant model specifications

Size of hidden layer          9
Sampling interval (s)         6.254e-5
No. of delayed plant inputs   3
No. of delayed plant outputs  2
Training samples              40000
Maximum plant input           240
Minimum plant input           0
Maximum interval value (s)    1
Minimum interval value (s)    0.5
Maximum plant output          120
Minimum plant output          0
Training Epochs               100
Training Function             Trainlm
Use current weights           selected
Use validation data           unselected
Use testing data              unselected

(ii) Controller design stage: The central idea of this type of control is to transform nonlinear system dynamics by canceling the nonlinearities. Fig. 6 shows the block diagram representation of NARMA-L2 controller. Sample performance graph and training data obtained from a NARMA-L2 controller are illustrated in Fig. 7 and Fig. 8 respectively.

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(iii) Simulink model of NARMA-L2 controlled Separately Excited DC Motor: The simulink model of a NARMA-L2 controlled separately excited dc motor is shown in Fig. 9. A simulink based plant model using PI controller is used to generate the required training data. The inputs of the controller are the reference speed and the actual speed and the output is the driving voltage to the motor.

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RESULTS AND DISCUSSION

NARMA-L2 controller has been successfully modeled and tested to control the speed of a separately excited dc motor. MATLAB 7.0 toolbox is used to model the system. The performance of the developed system is compared with two other systems; one with SimPowerSystems based chopper controlled dc motor model and the other one using simulink model. In chopper controlled circuits, a PI controller is used to generate the reference current and HCC is used to generate the switching patterns required by the chopper circuit. It has been found that the chopper and its control circuit could be eliminated by the use of NARMA-L2 controller. The validity of the system has been examined with different load torque, and different speeds. Simulation results are plotted as shown in Fig. 10. Plots of rated speed (wr), load torque (TL), actual speed of the motor using SimPowerSystems model (wap), simulink model (was ) and that of using NARMA-L2 controller (wasn) are shown in Fig. 10. Comparison of Figures 10.a, 10.b, and 10.c shows that NARMA-L2 controller is able to regulate the speed well above the rated conditions on the assumption that 50 % overload capacity is allotted.

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CONCLUSION

Speed controller system based on NARMA-L2 controller has been successfully developed using MATLAB to control the speed of a separately excited dc motor. The novelty of this paper lies in the application of NARMA-L2 controller to control of a separately excited dc motor. This paper also discusses modeling and control of SEDM using SimPowerSystems and simulink models. The performance of the system has been compared using different types of controllers. It has been found that NARMA-L2 controller is able to regulate the speed well above the rated values.