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This paper presents the speed control of a separately excited DC motor using Neural Network (NN) controller in field weakening region. In armature control, speed controller has been used in outer loop while current controller in inner loop is used. The function of NN is to predict the field current that realizes the field weakening to drive the motor over rated speed. The parameters of NN are optimized by the Social Spider Optimization (SSO) algorithm. The system has been implemented using MATLAB/SIMULINK software. The simulation results show that the proposed method gives a good performance and is feasible to be applied instead of others conventional combined control methods.

DC motors are used in many applications and industrial fields, because they can provide a high starting torque. It is also possible to obtain speed control over wide range below and above the rated speed [

The quivalent circuit of a separately exited DC-Motor is shown in

where

The main advantage of DC motors is simple in the speed control. As the motor angular speed

control method is used to control the speed of the SEDM below its rated speed. In this method, the field voltage

The motor angular speed

In this paper we propose a new method to increase the speed of the motor above the rated value (N rated). The new method based on predict the duty ratio (D) that give the field voltage

The structure of an artificial neuron is inspired by the concept of a biological neuron. Neural Network (NN) basically performs input output mapping which can be static or dynamic. One important feature of NN is that it normally requires supervised training (or learning) by input-output example data sets unlike conventional programming of digital computer [

where:

The weights sum

The sigmoid activation function is popular for neural network application. The equation for a sigmoid function is:

Feed forward network is a network of single neurons jointed together by synaptic connections.

A new type of evolutionary technique, which is called Swarm intelligence has enticed much research interest [

The SSO supposes that entire search space is a sectarian web, where all the social-spiders react to each other. Each solution within the search space symbolizes a spider position in the communal web. Every spider receives a weight relating to the fitness value of the solution that is denoted by the social-spider. The algorithm supposes two different search agents (spiders): males and females. Depending on gender, each person is behaved by a set of different evolutionary operators which mimic different mutual behaviors that are commonly assumed within the colony. The computational steps for the SSO algorithm can be abstracted as follows [

1) Considering N as the total number of n-dimensional colony members, define the number of male Nm and females N_{f} spiders in the entire population S.

2) Initialize randomly the female

and calculate the fitness of each individual.

3) Calculate the weight of every spider of S as follows:

where

4) Move female spider according to the female cooperative operator

The sectarian web is used as technique to transmit information among the colony individuals, this information is encoded as small vibrations. Each vibration depends on the weight and distance of the spider which has generated it.

There are two type of vibration:

a)

where

b)

where

The movement of the female is depended on the attraction and repulsion, and they are depended on a uniform random number

The new position

where

5) move the male according to the cooperative operator

We have now the third type of vibration

The male individuals are arranged in decreasing order according to their weight value, the individual whose weight

where

6) mating is performed by predominant males and females members, when a dominant male m locates at a set of females within a specific range r, it mates forming a new brood.

r is calculated as follows:

7) it stop condition is verified the algorithm is finished otherwise go back to step 3.

The flow chart of SSO is shown in

The SIMULINK model of Field Weakening Control for separately excited DC motor have been implemented using MATLAB/SIMULINK software as shown in

Nrated = 1750 r.pm | |
---|---|

J = 2.2 kg/m^{2} |

The speed and field current responses of the motor under and over the rated speed is shown in

Data No. | Normlized input (Nr − Nb)/Nb | Requirement field current (If) A | Duty ratio (D) | Data No. | Normlized input (Nr − Nb)/Nb | Requirement field current (If) A | Duty ratio (D) |
---|---|---|---|---|---|---|---|

1 | −0.71 | 1.6 | 1 | 6 | 0.088 | 1.46 | 0.91 |

2 | −0.428 | 1.6 | 1 | 7 | 0.137 | 1.4 | 0.87 |

3 | −0.142 | 1.6 | 1 | 8 | 0.191 | 1.33 | 0.83 |

4 | 0 | 1.6 | 1 | 9 | 0.25 | 1.26 | 0.78 |

5 | 0.043 | 1.53 | 0.95 | 10 | 0.314 | 1.2 | 0.75 |

The armature and field circuits of SEDCM are providing from separate sources; this can give a flexible control and wide speed rang. Field weakening methods are dependent on the reverse relationship between the field current and the speed of the motor. To control the speed of the motor from zero up to rated speed, armature control method is used and thus the motor drives in the constant torque region. While controlling the speed over the rated speed (the motor operates in the constant power region), field weakening methods should be used. In this work, the requirement field current has been predicted by using NN to drive the motor in both torque and power regions without needing any sensor to the armature voltage or field current. The parameters of NN optimized using SSO. The simulation results show the effectiveness of the proposed method.

Waleed I.Hameed,Ahmed S.Kadhim,Ali Abdullah K.Al-Thuwaynee, (2016) Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm. Engineering,08,1-10. doi: 10.4236/eng.2016.81001