This presentation describes the Design of Artificial Neural Network Controller for Power Electronics Converters and drives using MATLAB GUI

1. Dr.M.MURUGANANDAM,
Lecturer / Electrical Engineering, UTAS Ibri, Oman
Design of ANN Controller for Power Converter using MATLAB GUI

2. Design of ANN Controller
TOPICS TO BE DISCUSSED
Human Nerve System?
Structure of a Neuron
Biological Vs Artificial Neuron
A simple perceptron
Artificial Neural Networks
Learning Methods
Back propagation
ANN for AND operation
ANN for Power converter
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3. Human Nerve System
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How Many Nerves in our
body?
 Believe it or not, there are
over 7 trillion nerves in the
human body.
 All these nerves are part of
what's known as your body's
nervous system.
 You can think of nerves as
your body's electrical wiring
— they transmit signals
between your brain, spinal
cord, and the rest of your
body.
 The human brain has
approximately 86
billion neurons

4. STRUCTURE OF A NEURON
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A typical neuron may be divided into three distinct parts: its cell body,
dendrites, and axon

5. STRUCTURE OF A NEURON
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Dendrites (Receiver): Dendrites are projections of a neuron (nerve cell)
that receive signals (information / Message) from other neurons. The
transfer of information from one neuron to another is achieved through
electrochemical signals.
The Soma (cell body) : It contains the nucleus and cytoplasm. Biochemical
machinery and it sends signal.
Axon and Axon terminals: Axon is a conducting fiber and divides to axon
terminal, Axon terminals are the part of a nerve cell, that make synaptic
connections with another nerve cell.

6. STRUCTURE OF A NEURON
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Myelin: Myelin is an insulating fatty layer or sheath. It is made up of
protein and fatty substances. It speed up the, signal transmission along the
nerve cells.
Schwann: Schwann cell, also called Neurilemma cell, that produce
the myelin sheath around axons. Schwann cells are named after German
physiologist Theodor Schwann, who discovered them in the 19th century
Node of Ranvier : Node of Ranvier, periodic gap in the insulating sheath
that serves to facilitate the rapid conduction of nerve impulses
Nerves : it is made up of many neurons

7. BIOLOGICAL Vs ARTIFICIAL NEURON
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INSPIRATION FROM NEUROBIOLOGY
 A neuron has many-inputs / one-output unit
 output can be excited or not excited
 incoming signals from other neurons determine if the
neuron shall excite ("fire")
 Output subject to attenuation in the synapses, which
are junction parts of the neuron

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MATHEMATICAL REPRESENTATION
The neuron calculates a weighted sum of inputs and
compares it to a threshold and gives the output
Non-linearity

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A SIMPLE PERCEPTRON NETWORK
• It’s a single-unit network
• Change the weight by an amount
proportional to the difference
between the desired output and
the actual output.
Δ Wi = η * (D-Y).Ii
Perceptron Learning Rule
Learning rate
Desired output
Input
Actual output

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Artificial Neural Networks
Adaptive interaction: between individual neurons
Power: collective behavior of interconnected neurons
The hidden layer learns to
recode (or to provide a
representation of the inputs
associative mapping)

12. BACK PROPAGATION
 Desired output of the training examples
 Error = difference between actual & desired output
 Change weight relative to error size
 Calculate output layer error , then propagate back
to previous layer
 Improved performance, very common!
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13. LEARNING METHODS
Unsupervised
Reinforcement learning
Back propagation
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14. ONLINE / OFFLINE
• Offline
– Weights fixed in operation mode
– Most common
• Online
– System learns while in operation mode
– Requires a more complex network architecture
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15. Design of ANN controller for AND
Operation
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>> INPUT = [0 0 1 1 ; 0 1 0 1]
>> TARGET = [0 0 0 1]

16. MATLAB Command Window
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17. Import Input and Target Data
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18. Create Network
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19. AND Network
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20. Train Network
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21. Network Trained
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22. TEST 1
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23. TEST 2
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24. TEST RESULTS
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25. FUZZY LOGIC /ANN CONTROLLER FOR
DC DRIVE
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26. MATLAB CIRCUIT FOR FUZZY
CONTROLLED DC DRIVE
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27. Data extraction from FLC
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28. Extracted data
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e ce dc
1 1 -0.00048 200020
2 0.857341 -0.00044 -19159.8
3 0.733414 -0.00038 -10409
4 0.627149 -0.00031 -4932.02
5 0.535603 -0.00028 -7336.97
6 0.457222 -0.00024 -6190.21
7 0.389883 -0.00022 239.0803
8 0.332035 -0.00016 -12555.8
9 0.278306 -0.00013 -8088.84
10 0.970612 -0.00047 -16248.9
11 0.830583 -0.00041 -13105.2
12 0.710667 -0.00036 -1783.72
13 0.607456 -0.00031 -3908.89
14 0.518759 -0.00028 -6113.31
15 0.442654 -0.00023 -2417.42
16 0.377402 -0.00019 -2357.47
17 0.321224 -0.00017 -6805.21
18 0.256247 -0.00013 -9322.09
19 0.940465 -0.00045 -10109.1
20 0.804701 -0.00038 -7768.73

29. ANN- Network
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30. View Weights
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31. Structure of Neural Network
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32. Neural Network in MATLAB
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33. MATLAB CIRCUIT FOR ANN FED DC
DRIVE
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34. Design of ANN Controller
THANK YOU
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