Speed control of separately excited DC motor

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

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Hysteresis current control is a method of controlling a power electronic converter so that an output current is generated which follows a reference current waveform. A hysteresis current controller is implemented with a closed loop control. The difference between the desired current, and the current being injected is used to control the switching of the chopper circuit. When the error reaches an upper limit namely upper hysteresis limit, GTO is switched to force the current the current down. On the other hand when the error reaches the lower hysteresis limit, a positive pulse is produced to increase the current. The minimum and maximum values of the error signal are [e.sub.min] and [e.sub.max]. The range of the error signal, [e.sub.max]-[e.sub.min], directly controls the amount of ripple in the output current and is called the hysteresis band. Thus the armature current is forced to stay within the hysteresis band determined by the upper and lower hysteresis limits.

b) Modeling and control of SEDM using Simulink model: The speed control circuit of a SEDM using Simulink is shown in Fig. 4, where

[V.sub.t]  - Supply voltage (V)
[E.sub.b]  - Back emf (V)
[R.sub.a]  - Armature resistance ([omega])
[L.sub.a]  - Armature inductance (H)
[R.sub.f]  - Field resistance ([omega])
[L.sub.f]  - Field inductance (H)
[I.sub.f]  - Field current (A)
[I.sub.a]  - Armature current (A)
[w.sub.m]  - Speed (rad/s)
J          - Rotor inertia of motot ([kgm.sub.2])
[D.sub.m]  - Viscous friction of motor (Nms)

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In Fig.4, the GTO is modeled using a switch. The switch block has three inputs: the middle input controls which of the two other inputs is routed to the output. If the control input is one, 240 V is routed to the output, on the other hand if the control input is zero, a zero will be routed to the output.

c) The Speed control of SEDM using NARMA-L2 controller:

NARMA-L2 Controller: The learning ability, self-adapting, and super-fast computing features of ANN make it well suited for the control of electrical power systems in many applications such as: electric load forecasting, transient ability assessment, active power filter, dynamic voltage restorer, and unified power quality conditioner. In learning process, neural network adjusts its structure such that it will be able to to follow the supervisor. The learning is repeated until the difference between network output and the supervisor is low.

(i) System Identification Stage: NARMA-L2 controller, a multilayer neural network has been successfully applied in the identification and control of dynamic systems (32). System identification and control design are the two steps involved in using NARMA-L2 controller. In the system identification stage a neural network model of the plant to be controlled is developed. Fig. 5 shows the block diagram representation of the system identification stage. In the control design stage, the neural network plant model is used to train the controller.