CHAPTER 4 FIELD ORIENTED CONTROL SYSTEM USING SOFT...

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CHAPTER 4

FIELD ORIENTED CONTROL SYSTEM USING SOFT COMPUTING TECHNIQUES

4.1 Introduction

The high performance IM drive system using FOC is characterized by [71]

a. Fast step tracking response without overshoot

b. Minimum speed dip and restore time due to a step load change

c. Achievement of zero steady state error in the command tracking and load

regulation

However, the small speed dip with short restore time will lead to large overshoot and

settling time. Also, in the high dynamic performance control schemes such as FOC

system, the flux is maintained constant and hence IM runs efficiently at its rated

values. When the load is reduced or parameter variation occurs, the efficiency and the

energy consumption are adversely affected because the IM is multivariable, complex

and nonlinear as seen from its d-q model. The FOC system using PI controllers is

developed to control the parameter variation in the outer speed control loop [72]. The

controller generates command current directly proportional to the required torque;

hence variation in the load can also be compensated. The conventional PI controllers

are fixed parameter controllers and changes in rotor time constant will degrade the

performance of the drive system. It is difficult to design a high performance PI

controller to handle the variation in parameter and load.

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Artificial Intelligence (AI) Techniques have extensive application in different fields

[73]. AI techniques have found many applications in most nonlinear systems like IM

drives. Advanced control based on AI technique is called intelligent control. Every

system with intelligent control is called self-organizing or autonomous system [74,

76-77]. The invention of high power, high speed and low cost power electronic

switches such as IGBT and IGCT [75], invited the intelligent control in MC drive

applications [71]. The AI techniques are divided into two groups: hard computation

and soft computation. Expert system belongs to hard computation which has been

considered the first AI technique implemented in science but not in electric drive

applications [76 - 77].

In past two decades, the growth in soft computing technologies has provided

sophisticated methodology for the development of industrial process controllers. It is

considered to be a state of art approach to artificial intelligence. Within the last

decade, substantial growth of soft computing techniques in IM drive application has

been made because the soft computing techniques do not require the mathematical

model of the system. Soft computing techniques have been recognized as attractive

alternatives to the standard, well established hard computing paradigms [78]. Soft

computing techniques, in comparison with hard computation employ different

methods which are capable of representing imprecise and uncertain computation

methods. Soft computing techniques are capable of handling non-linear systems such

as IM drives and offer computational simplicity. The major soft computation

techniques applied in Industrial applications are

a. Fuzzy Logic System

b. ANN

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c. Genetic Algorithm Based System

d. Fuzzy-Neural Network

Fuzzy Logic can be used for modelling non-linear, unknown or partially known

controllers. It emulates human reasoning providing an intuitive way to design a

functional block for a control system. ANN is an information processing system and

consists of number of highly interconnected neurons. The neurons behave like their

biological pattern, neural cell in brain. ANN is based on learning process. The

learning process changes the synaptic weight of each interconnection in the network

and updates it until the target error is reached. Genetic Algorithm is a search heuristic

that mimics the process of natural evolution. The Fuzzy- Neural Network (FNN)

approach incorporates the Fuzzy logic controller into the neural network structure.

Neural Network provides the connectionist structure and learning ability to Fuzzy

controller [77 - 80]. In this chapter, the implementation of Fuzzy logic control, ANN

control, Adaptive Neuro Fuzzy Inference System and FELM Algorithms in MC drive

for IM using FOC system is discussed in detail. The simulation model of each method

is presented and the performance of each method in selecting the switching state for

MC is also discussed.

4.2 Fuzzy Logic Control System

Fuzzy logic system is based on the theory of Fuzzy sets and uses linguistic variables.

The inputs are transformed into Fuzzy sets and this process is called fuzzification

using membership functions. The fuzzified inputs are then processed by a set of Fuzzy

rules to produce the output and this is called Fuzzy Inference System (FIS). There are

two FIS systems available namely: 1. Mamdani FIS and 2. Sugeno FIS [75]. At the

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output level, Mamdani system uses distributed Fuzzy set and Sugeno system uses

constant or linear output membership functions.

The inputs to the Fuzzy controller are error and change in error values. The controller

observes the pattern of these two input signals and generates corresponding output

signal using Fuzzy inference system. The fuzzified inputs and output are usually

handled in per unit (pu) form by using respective scale factors. The output signal is

then integrated to generate the actual control signal [74]. The generalized Fuzzy

inference system is shown in Fig.4.1.

Fig.4.1: Generalized Fuzzy Inference System

The modified version of PI is shown in Fig.4.2. In the FOC system using MC drive,

there are 3 Fuzzy logic controllers used as shown in Fig.4.2. The inputs to FLC1 is

the error between the rated rotor flux, ids* and ids. The inputs to FLC2 are iqs

* and iqs,

where iqs* is the command generated from the outer speed control loop. The speed

control loop uses PI controller and generates the command signal iqs* from the

reference speed and the motor speed signals. The membership functions of error (e),

change in error (δe) and the change in output (co) are shown in Fig.4.3. Fig.4.4 shows

the surface view of the rules. The inputs and output have 5 membership functions.

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The same is true for FLC2. The rule table for FLC1 is shown in Table 4.1. The third

FLC3 selects the switching vector for the MC. The inputs to FLC3 are error in the

phase current value (ie), error in phase voltage value (ve) and the switching sector (d).

The error in the phase current value (ie) is calculated by comparing the input current

with the stator current. The error in phase voltage (ve) is calculated by comparing the

output voltage with the reference voltage. The switching cycle interval is calculated

from the angles of output voltage vector, input current vector and input phase

displacement angle as shown in chapter 2. The current error (ie) and the voltage error

(ve) are divided into 6 triangular membership functions. The switching cycle (d) is

divided into 4 triangular membership functions. The output membership function has

21 triangular membership functions to select the switching states for MC. The change

in output value produced represents the switching configurations. The defuzzification

method used is centre of output area method. The switching configuration then uses a

look up table to find the corresponding to the switching vector for MC. After

developing the control algorithms for FLC1, FLC2 and FLC3 in Mamdani method,

they are incorporated in Simulink simulation system as shown in Fig.4.3. The drive

system was simulated in Matlab/Simulink and the performance of IM at various

reference values are compared with the drive system using conventional PI

controllers.

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Fig.4.2: Simulation Model of Fuzzy Switch State Selection in FOC

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Fig.4.3a: Input Membership Functions for e and δe

Fig.4.3b: Output membership function ‘co’

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Table 4.1: Fuzzy Rules for FLC1

d∆e ∆e

NL NS ZE PS PL

NL NL NL NS NS ZE

NS NM NS NS ZE PS

ZE NS NS ZE PS PS

PS NS ZE PS PS PL

PL ZE PS PS PM PL

Fig.4.4: Surface View of the Rules

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4.2.1 Simulation Results

The simulation results are shown in Fig.4.5, Fig. 4.6 and Fig.4.7. The reference speed

increased to 800 rpm and maintained at 800 rpm until 1 sec. and then reduced to zero.

The torque is maintained at 390 Nm until 0.9 sec. When the speed transition is

initiated at 1sec, the torque changes to 200 Nm and it is maintained at this value until

1.7sec. Then the torque is changed to +500 Nm at 1.8sec and it is maintained at this

value until 2 sec. After 2 sec, the speed transition is completed and the torque settles

at -600 Nm. From Fig.4.5, it can be seen that the speed curve follows the reference

curve. The speed curve reaches the reference value of 800 rpm at 0.9 sec. with very

less overshoot compared to the conventional system discussed in chapter 3.

Fig.4.6 shows the torque curve where the initial torque increases linearly and reaches

the maximum value of 390 Nm during speed transition. At 0.9 sec., when the speed

curve reaches the steady state value of 800 rpm, the torque value settles at 0 Nm.

When the speed curve reaches the reference value of 0 rpm at 1.8 sec, the torque

curve reaches the reference value of 500 Nm and stays at 500 Nm until 2 sec. The

THD of the stator current is shown in Fig.4.7. The THD of the stator current is high at

48.17 but is less compared to conventional system discussed in chapter 3. In Fuzzy

system, the ripples in the speed curve and the overshoot is reduced very much,

whereas the torque ripples around the steady state values are maintained at the same

level as that of conventional controller.

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Fig.4.5: Speed Vs Time Curve

Fig.4.6: Torque Vs Time

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Fig.4.7: THD of Stator Current

4.3 ANN Control System

Neural Networks represent a very sophisticated modeling technique that can model

extremely complex functions. The ANN is a powerful tool that can work out the

nonlinear relationships between the input and output. The artificial neuron receives

number of inputs. The neurons are highly interconnected and are connected by

weighted links. The weighted links carry the signal. Each neuron has a single

threshold value. The weighted sum of the input is formed and then subtracted from

the threshold value to get the activation signal of the neuron. The activation signal is

passed through an activation function to produce the output signal. The ANN

technique is based on learning process. The use of ANN improves the performance of

system control. Hence it is widely used for applications in power electronics.

Generally multi-layer feed forward network trained by back propagation method is

used to calculate the output. The training process changes the synaptic weight of each

interconnection and updates it until the target error value is achieved. The back

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propagation method uses 3 layers. The first layer is called input layer which receives

the input pattern. The inputs propagate forward until they reach the output units where

they produce the actual output or predicted output pattern. The outputs are presented

in the output layer. While propagating, the inputs pass through many layers called

hidden layers where the weights are adjusted to reach the target error value. The back

propagation algorithm is a supervised learning system. Hence the desired outputs are

given during training. The actual output is then subtracted from the desired output to

get the error signal. The error signals are then passed back through the neural network

to adjust the weights and to compute the new error value. This process continues until

the error saturation occurs. After the completion of training process, the offline

performance of the network with arbitrary input pattern is tested. After ensuring

satisfactory results, the weights and biases are downloaded to the neural controller

and are inserted in the FOC system to produce the switching vectors.

The inputs to the ANN network are Va, Vb and Vc. The outputs from the ANN

network are the switching voltage vectors Vab, Vbc and Vca. The training algorithm for

neural network is shown in Fig.4.8.

4.3.1 Simulation Diagram

The FOC system using ANN as switching state selector for MC is simulated using

Matlab/Simulink. The direct AC-AC MC is designed with nine bi-directional IGBT

switches as shown in Fig. 2.10. The complete MC system designed with SVM

algorithm is shown in Fig. 2.15. The d-q model of IM is designed as shown in Fig.3.2

to analyze the rotor flux and stator current. The ANN controller is designed with three

layers and Back Propagation Algorithm is used for training procedure. The training

in

m

p

nputs are tak

model of M

erformance

ken from the

MC drive sy

of IM is test

e standard S

ystem using

ted for vario

Fig.4.8: AN

VM based M

g ANN con

us reference

NN Training

MC system.

ntroller is s

e speed and t

g Algorithm

The comple

shown in F

torque value

m

8

ete simulatio

Fig. 4.9. Th

s.

81

on

he

82

Fig. 4.9: Complete System with ANN Controller

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4.3.2 Simulation Results and Discussion

The simulation results are shown in Fig. 4.10, Fig. 4.11 and Fig. 4.12. The reference

speed is increased to 800 rpm and maintained at 800 rpm until 1 sec. and then reduced

to zero. During speed transition from 800 rpm to 0 rpm from 1sec. to 1.8 sec., the

torque is changed to +500Nm and maintained at 500 Nm until 1.7 sec. At 2 sec., the

torque is changed to -800 Nm. From Fig.4.10, it can be seen that the speed curve

follows the reference curve with fluctuations. The speed curve reaches the reference

value of 800 rpm at 0.8 sec. with very less overshoot compared to the conventional

system but is higher compared to Fuzzy system. The ripples and overshoot present in

the speed curve are due to the fact that the inputs to the ANN controller are not

fuzzified.

Fig.4.11 shows the torque curve where the initial torque reaches the maximum value

of 300 Nm and settles at 300 Nm during speed transition. At 1sec., when the speed

curve reaches the steady state value of 800 rpm, the torque value settles at 0 Nm.

When the speed curve reaches the reference value of 0 rpm at 1.8sec., the torque

curve reaches the value of 800 Nm at 1.8 sec. and stays at 800 Nm until 2sec. The

THD of the stator current is shown in Fig.4.12. The THD of the stator current is high

at 42.57 but is less compared to conventional system discussed in chapter 3. In the

ANN System, the ripples and overshoot in the speed curve is high compared to Fuzzy

system, whereas the presence of torque ripples around the steady state values are

reduced due to the training nature of ANN System and improves the efficiency of the

converter. The amount of THD present in the stator current is measured for 7 cycles

and it is found that the THD is less compared to conventional and Fuzzy System.

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Fig.4.10: Speed Vs Time curve


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Fig.4.12: THD of Stator Current

4.4 Adaptive Neuro-Fuzzy Inference System

As mentioned in earlier section, Fuzzy logic is well suited for dealing with ill defined

and uncertain systems. In Fuzzy interface system, the Fuzzy if-then rules resemble

human like thinking. The Fuzzy control system does not require any quantitative

analysis but it requires the details about number of inputs and outputs to design

membership functions and their shapes and the knowledge about how the inputs are

processed to produce the required output to design Fuzzy rules. The ANN network can

be used to model large classes of non-linear structures by using learning procedure.

But the learning procedure is very long and it reduces the accuracy of the output in the

online process control system. The ANFIS is one of the inference systems where the

advantages of Fuzzy logic and ANN are combined.

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ANFIS (ANFIS) has been proposed for the first time in [81 - 82]. Using the prior

knowledge about the output of the system, ANN can be trained online or offline

learning process. It uses the training data to build the Fuzzy system in which the

membership functions are adjusted using the back propagation algorithm. The

accuracy of the system depends on the training set data. In the ANFIS system, the

membership functions and their shapes need to be defined first and then the input and

output training data set should be imported. The ANFIS will study the input output

training data set to understand the system behavior. The ANFIS should have a good

data distribution to interpolate all necessary information to understand the system

operation.

The ANFIS structure is composed of five functional blocks namely rule base, database,

decision making unit, fuzzification interface and defuzzification interface. Fig.4.13

shows an ANFIS structure which has two inputs and one output. The two input system

requires four Fuzzy if-then rules in Sugeno. As shown in the ANFIS structure, the

inputs are mapped using membership functions by their synaptic weights and bias and

produces output membership functions and corresponding synaptic weights and bias.

The ANFIS structure uses Sugeno Fuzzy inference system because of its constant or

linear membership function at the output level and also it is computationally more

efficient than the Mamdani method.

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The ANFIS structure contains five network layers

Fig.

4.13

: T

wo

Inpu

t AN

FIS

Stru

ctur

e [8

3]

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Layer 1: Every node in this layer contains membership function. Usually these are

chosen triangular or bell shaped functions, where the number of them depends on

control object. The parameters can be tuned by back propagation algorithm. The first

phase generally can be written as:

(4.1)

where,

i = input number

j = membership function number in ith input

k = node number in present layer

xi = input signal

= first layer output

= membership function

The node number is given as

K = IJ (4.2)

where, I = number of inputs and J = number of membership functions

Layer 2: At layer 2, the input signals are multiplied by their respective weights and

mapped through Fuzzy membership functions. This layer corresponds to the min

calculation in classical Fuzzy logic system. It can be written as

min , (4.3)

where,

is the second layer output, with condition .

x1 and x2 are two inputs of the system

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Every node is not connected together as in neural network classical structure. The

connections are between outputs of membership functions with different inputs.

Layer 3: Layer 3 uses Fuzzy rules to select the output membership functions. Every

node of this layer calculates the weight and is normalized. The output results are in the

range of 0 and 1. It can be written as

∑

(4.4)

where, is the output of third layer

Layer 4: The fourth layer is the decision making layer. The output membership

functions are defuzzified. Every node in this layer is a connection point with the node

function:

, ∑ (4.5)

where,

is the output of fourth layer and

is the consequent parameters

The linear class of function has been chosen to simplify the learning process. The

consequent parameters of the functions can be tuned by back propagation algorithm

and also can be identified by least square estimate.

Layer 5: The last phase of ANFIS is summation of all incoming signals. It combines

all the output to produce the crisp values. The result of this node is the control signal.

The calculation can be written as

90

∑ (4.6)

Training the ANFIS is actually tuning the weights to reduce the target errors. Based on

the tuning, the width of the membership function of the ANFIS structure gets changed

and hence the accuracy of the system is improved. The neuro-Fuzzy structure is

initially trained to get the error at the minimum required level. The trained network can

be used in the system to model non linear functions, identify non-linear components

and predict the output.

The ANFIS controller is used here to select the switching states for MC. The inputs to

the ANFIS controller are flux error and speed error. The proposed ANFIS structure

composed of five memberships function and governed by 25 rules.

The ANFIS layers and rules are shown in Fig.4.14. The complete ANFIS based FOC

system is shown in Fig.4.15.

The surface view of the rules is shown in Fig.4.16. The output of the ANFIS controller

generates Vsd and Vsq voltages. These voltages are converted into three phase voltages

and fed as reference voltages into the MC. Using the algorithm described in section

4.2 required duty cycles can be calculated. The switches will be turned on and off

according to the duty cycle and hence the frequency and amplitude of output voltage

can be altered to achieve the reference speed.

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Fig

4.14

: A

NFI

S la

yers

and

Rul

es fo

rmat

ion

92

Fig

4.15

:AN

FIS

base

d FO

C fo

r M

C

93

Fig.4.16: Surface View of Fuzzy Rules in ANFIS


The simulation results are shown in Fig.4.17, Fig.4.18 and Fig.4.19. The

reference speed is increased to 800 rpm and maintained at 800 rpm until 1 sec. and

then reduced to zero. The torque is changed to +500 Nm at 1.8 sec. and

maintained at +500 Nm until 2 sec. After 2 sec., the torque is changed to -500

Nm. From Fig.4.17, it can be seen that the speed curve follows the reference curve

with small fluctuations. The speed curve reaches the reference value of 800 rpm at

0.9 sec. without any overshoot. It can be seen from the torque curve shown in Fig.

4.18 that the initial torque value is reduced to 300 Nm. The torque curve settles at

300 Nm at 0.1sec. When the speed curve maintains the steady state value of 800

rpm and the torque value settles at 0 Nm. When the speed curve reaches the

reference value of 0rpm at 1.8sec, the torque curve reaches the value of 500 Nm

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and stays at 500 Nm until 2 sec. The THD of the stator current is shown in

Fig.4.19. The THD of the stator current is high at 41.06 but is less compared to

conventional system discussed in chapter 3. In the ANFIS system, the ripples and

overshoot in the speed curve are highly reduced, whereas the presence of torque

ripples around the steady state values is eliminated due to the training nature of

ANN system and improves the efficiency of the converter. The amount of THD

present in the stator current is measured for 7 cycles and found that it is less than

that of the other systems.

Fig.4.17 Speed Vs Time

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Fig.4.19: THD value of Stator Current

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4.5 FELM Algorithm

In the recent past, Huang et al., have proposed a new learning algorithm called the

ELM; it is a single-hidden layered feed forward neural network (SLFN). Huang, G.B.,

et al., [85] says that ELM may randomly choose and fix all the hidden node

parameters and then analytically determine the output weights.

Once the weights of the SLFNs have been randomly assigned, then SLFNs is to be

considered as a linear system; the output weights can be obtained analytically through

a generalized inverse operation of the hidden layer output matrices. The activation

functions used in ELM are any non-linear activation function used in neural network

(sigmoid, hyperbolic function, etc.), radial basis function, complex activation function

[85], and so on.

The proposed SLFN can have P hidden nodes and it can be approximated through the

given N pairs of input / output values, namely, , ) x with zero error,

then we have

∑ , , for j= 1, 2, …. P (4.7)

where (ai, bi) is the parameter associated with ith hidden node and I is the output

weight linking the ith hidden node to the output node. In this paper, non-linear

activation function, called sigmoid function is used. That is,

, , . , . (4.8)

Hence, equation (4.7) can be rewritten as

H = T (4.9)

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where

, , , , … , , , , , , … , ,

.

. , , , , … , ,

, , … , T and , , … , T

While computing, = H# T is used as the estimated value of , where H# is the

Moore-Penrose generalized inverse of the hidden layer output matrix H [86]. The

following is the formal ELM algorithm proposed by Huang et al.,[87].

4.5.1 ELM Algorithm

Given a training set of input / output values , ) x , for i = 1, 2, …, N;

the activation function , , . , and the number of hidden

nodes P.

Step 1: By using continuous sampling distribution, assign random hidden nodes by

randomly generating parameters (ai, bi) for i = 1,2, …, N

Step 2: Compute the hidden layer output matrix H

Step 3: Compute the output weight , by using the relation = H# T

4.5.2 FELM Based Optimal Switch State Selection

The switch states can be selected using FELM Algorithm. The outputs from the Fuzzy

current controllers are Vds* and Vqs

*. These outputs are transformed into Va, Vb and Vc

using inverse part transformation. These inputs are used to select the optimal switch

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state by applying FELM Algorithm. The controller action is explained in the following

steps.

i. Fuzzy variables Vao*, Vbo

* and Vco* are divided into five linguistic variables

namely, Negative High, Negative Small, Zero, Positive Small and Positive high.

The Fuzzy membership functions for these variables are shown in Fig.4.20.

ii. These Fuzzy input values are fed into ELM. Then, ELM structure is trained first

with a set of training input / output data. The Fuzzy output data are collected

according to Mamdani Fuzzy inference system followed by max-product

principle. Once Fuzzy-ELM is trained with sufficiently large number of training

data set, the training will be stopped at ith iteration. It is observed that the error is

1e-05. This is shown in Fig.4.21.

iii. The Fuzzy output variable used here is switching vector selection for MC. The

Fuzzy membership function for the output variable (SW1) is given in Fig.4.22.

iv. After training ELM, the switches to be fired are selected based on the Fuzzy

decision matrix. The error performance analysis plot is shown in Fig.4.23. Switch

state selection flow diagram is shown in Fig.4.24. It can be seen that in the ELM

method the percentage of error is less compared to conventional methods.

The design of complete FELM based switch state selection for MC is shown in

Fig.4.25.

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Fig.4.20: Fuzzy Membership Function for one of the Input Variables Vao*

Fig.4.21: ANN Error Signal Output

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Fig.4.22: Fuzzy Membership Function for Output Variable

Fig.4.23: Performance Analysis Plot

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Fig.4.24: Switch State Selection Flow Diagram

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Fig.4.25: Complete Block Diagram of Fuzzy-ELM system


The simulation results are shown in Fig.4.26, Fig.4.27 and Fig.4.28. The reference

speed is increased to 800 rpm and maintained at 800 rpm until 1 sec. Then it is reduced

to zero. The torque changes to +500 Nm until 2 sec. After 2 sec., the reference torque

is changed to -500 Nm. From Fig.4.26, it can be seen that the speed curve follows the

reference curve without any fluctuations. The speed curve reaches the reference value

of 800rpm at 0.7sec without any overshoot and follows the reference speed without

any fluctuations compared to the conventional system and other intelligent systems.

Fig.4.27 shows the torque curve where the initial torque reaches the maximum value of

900 Nm and settles at 300 Nm during speed transition. At 1sec., when the speed curve

settles at the steady state value of 800 rpm, the torque curve settles at 0 Nm. When the

speed curve reaches the reference value of 0rpm at 1.8 sec., the torque curve reaches

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the value of +500 Nm and stays at +500 Nm until 2 sec. The THD of the stator current

is shown in Fig.4.28. The THD of the stator current is reduced to 6.96 which is very

low compared to the rest of systems discussed earlier which improves the efficiency of

the converter. The torque curve reaches a high value during each transition which

could be avoided by improving the accuracy of the learning system.

Fig.4.26: Speed Vs Time

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Fig.4.28: THD value of Stator Current

4.6 Results and Discussion

The performance of intelligent controllers in selecting the switching state for MC and

hence the speed control operation of the IM is discussed from the simulation results

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presented in earlier sections of this chapter. The comparative analysis of the

controllers is presented in Table 4.2

4.7 Conclusion

The basics of Soft Computing Techniques such as Fuzzy, ANN and ANFIS are

presented in this chapter. In addition, the implementation of Fuzzy, ANN and ANFIS

techniques in selecting the switch state for MC in FOC system is discussed. The

Matlab implementation of each method is also presented in this chapter.

Table 4.2: Comparative Analysis of Conventional, Fuzzy, ANN, ANFIS and

FELM controllers in FOC System

Parameters

ConventionalPI system

Fuzzy System

ANN System

ANFIS System

FELM System

Starting Current(A)

850 1500 1500 1600 1500

Efficiency (VTR)

79 85 87 88 83

THD (fs = 2kHz)

67.3 48.7 42.57 41.06 9.85

Rise Time (sec)

1 0.8 0.8 0.7 0.6

Overshoot

Very high less high Very less Very less

Initial Torque (Nm)

750 350 300 300 900

Torque Oscillations

+500 to ‐900 +600 to ‐700 +800 to ‐900 +500 to ‐580 +900 to ‐750

Pulsating components (torque)

High Very high Very high Very less Very less

Permissible error

‐‐ ‐‐ 1e‐05 1e‐5 1e‐5

No. of epochs takento reach the

target

‐‐ ‐‐ 2642 1359 694

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The design of Fuzzy controller involves design of membership functions for input and

output variables. Rules are designed based on the observed performance of the

converter using SVM method in the previous chapter. The implementation of Fuzzy

logic technique in selecting the switch state for MC proves to be an alternative to the

slow performing PI controller based SVM method for switch state selection. The

response time of the FOC system is improved with no overshoot. It has been observed

that the Fuzzy controller is more sensitive to load changes.

The design of the ANN controller involves training the network using back

propagation. The training data for switch state selection is obtained from the

conventional SVM method discussed in the previous chapter. After achieving the

minimum error, the trained network is inserted in the simulation model to select the

switching state for MC in FOC system. Due to the training process it has been

observed that the switching pulses are produced with accurate timing. Also, the

computation of switching pulses is completed during one switching period. The

implemented ANN based switching state selection for MC system in FOC proves to

be best alternative for conventional SVM method. Also, the ANN controller

performance is not affected by load changes because of the training process involved

in controller design.

The design of ANFIS controller combines design knowledge of Fuzzy and ANN for

switch state selection for MC in FOC system. The ANFIS controller uses Sugeno

method which is more flexible for non linear control applications. The control

strategy was developed by writing Fuzzy rules. The proposed ANFIS controller uses

back propagation algorithm. The performance of the FOC system using ANFIS

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controller is faster than the other methods. Since the inputs are fuzzified and also the

training is involved the system becomes less sensitive to input and load changes.

The FELM Algorithm is implemented as a novel technique in selecting the switch

states for MC in FOC system. The ELM Controller is successfully implemented along

with Fuzzy speed and current controllers in the field oriented closed loop control

system. It can be seen from the analysis and results that the switching vectors for MC

are selected perfectly by the ELM algorithm and hence the speed control of IM is

achieved with less overshoot and torque ripples. The implementation of Fuzzy speed

and current controllers eliminates the unwinding effect of PI controllers in

conventional system. Even though the time taken by the speed curve to reach the

steady state value is significantly less, it can be improved by improving the flux

observer model in the system. The quick response of the ELM controller is based on

the non-linear network optimization obtained through deepest descent method and

least square estimations used for both back-propagation and hybrid learning

techniques with error accuracy 1e-04. The advantages of the proposed method are:

a. the deterministic and probabilistic approaches are used together to determine

the switching state vector for the MC

b. it guarantees to achieve a local optimum which is close to the global optimum.

CHAPTER 4 FIELD ORIENTED CONTROL SYSTEM USING SOFT...

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