Automatic Control

International Review of

Automatic Control (IREACO) Theory and Applications

Contents Adaptive Neuro-Fuzzy Speed Regulator Applied in Direct Torque Control for Induction Motor Drive Using Multilevel Inverter by Zegai M. L., Bendjebbar M., Belhadri K., Lakhdari F.

182

New Generator and Shunt Reactive Power Control Based Secondary Voltage Control Approach by S. K. S. Abdullah, M. K. N. M. Sarmin, N. Saadun, M. T. Azmi, I. Z. Abidin, I. Musirin

192

Enhanced Sliding Mode MPPT and Power Control for Wind Turbine Systems Driven DFIG (Doubly-Fed Induction Generator) by M. Reddak, A. Berdai, A. Gourma, J. Boukherouaa, A. Belfiqih

207

Robust Model Predictive Control Applied to a WRIG-Based Wind Turbine by A. El Kachani, E. Chakir, T. Jarou, A. Ait Laachir, J. Zerouaoui, A. Hadjoudja

216

Sensorless DTC Drive of Induction Motor Using 3-Level Inverter by A. Achalhi, M. Bezza, N. Belbounaguia

227

Design of Adaptive Control Technical Systems with Limited Uncertainty Based on Exo-Model by Vladimir E. Kuznetsov

234

Tracking Control of Quadrotor Using Static Output Feedback with Modified Command-Generator Tracker by Trihastuti Agustinah, Feni Isdaryani, Mohammad Nuh

242

Diagrammatic Modeling Language for Conceptual Design of Technical Systems: a Way to Achieve Creativity by Sabah Al-Fedaghi

252

System of Computer Vision for Cold-Rolled Metal Quality Control by Batischev V. I., Kuzmin M. I., Pischukhin A. M., Solovyov N. A.

259

Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved

International Review of Automatic Control (I.RE.A.CO.), Vol. 9, N. 4 ISSN 1974-6059 July 2016

Adaptive Neuro-Fuzzy Speed Regulator Applied in Direct Torque Control for Induction Motor Drive Using Multilevel Inverter Zegai M. L., Bendjebbar M., Belhadri K., Lakhdari F. Abstract – Direct Torque Control is one of the latest techniquesof induction motor drive control. DTC permitsto obtain a quick torque and speed responses without complex orientation transformation and any inner loop current control, but its main drawbacks are in the torque ripple and the current distortion. For solvingthese problems, this paper presents a DTCwitha multilevel inverterto control the induction motorappliedto an adaptive neuro-fuzzy speed controller.The results of the newproposed controllerare compared to those obtained using a conventional PID. The system modeling and simulations are implemented in MATLAB/SIMULINK environment. Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Direct Torque Control, Induction Motor, Multilevel Inverter, Neuro-FuzzySpeed Controller

Direct stator flux Quadrature stator flux Direct rotor flux Quadrature rotor flux The angle of stator flux The angle between stator and rotor flux

Nomenclature ANFIS ANN DTC

e (t) FIS FL

Ids Iqs IM

KD KI KP Ls Lr Lm NPC P PI PID PV Rs SP t THD Te Te* Vds Vqs

Adaptive neuro fuzzy inference system Artificial neural network Direct torque control The flux error The torque error Speed error Fuzzy inference system Fuzzy logic The torque hysteresis band The flux hysteresis band Direct stator current Quadratic stator current Induction Motor Stator current vector Rotor current vector Derivative gain Integral gain Proportional gain Stator self inductance Rotor self inductance Mutual self inductance Neutral Point Clamped Number of pole pairs Proportional integral Proportional integral derivative Measured process variable Stator resistance Desired set point Instantaneous time Total Harmonic Distortion Measured torque Torque of reference Direct stator voltage Quadrature stator voltage

I.

Introduction

Induction motors are electro-mechanical devices used in most of applications for power conversion from the electrical to the mechanical form [1]-[38]. The induction motors are found in wide industrial applications due to their simple construction, reliability, ruggedness, low cost and they can be used in aggressive environments [2]. Many techniques have been applied to induction motors control [4]-[5], among them, DTC appears to be very convenient for controlling this type of motors. The DTC was proposed in first time by I. Takahashi [6] and M. Depenbrock [7], recently this technique has gained a success due to its capability of producing a fast induction motor torque control [3], [34], [37]. The basic idea of this technique is the control the stator flux and the electromagnetic torque [8]. By compared to other techniques, the advantages of the DTC can be resumed in the fact that it does not need a complicated coordinate transformation, or current loop regulation, or modulation block, and it offers a high robustness against motor parameter variations. But in other hand its main drawbacks are in the high electromagnetic torque ripples [15], and current distortion caused by sector changes. To eliminate the above difficulties and for improving this control strategy, many investigations have been published in literature, in [2], [9]-[13] and [17]-[19], for reducing the torque ripples, the authors suggested a DTC


DOI: 10.15866/ireaco.v9i4.9149

182

Zegai M. L., Bendjebbar M., Belhadri K., Lakhdari F.

using the three level inverter ([37]), for the same object, different schemes of DTC are proposed, where the PI speed controller is replaced by an intelligent controller such as fuzzy logic in [1], [3], [8], and artificial neural networks in [28]; other authors proposed to create the main algorithm of DTC with Neuro fuzzy controller using tow levels inverter in [29] and three levels inverter in [9]. The FL is well suited for dealing with uncertain systems where is utilizing the fuzzy if-then rules, which are very familiar to human thinking method without using any precise quantitative analysis, and ANN has a remarkable performances, as it offered a good map approximation ability, self-learning, and self-organization ability [29], and the characteristics of both FL and ANN can be useful to produce the Neuro fuzzy controller. This paper, presents a Direct Torque control using a three level NPC inverter with an adaptive Neuro-Fuzzy speed controller, to achieve a better performance with high reliability, where the multilevel inverters are progressively being used in high-power medium voltage applications due to their superior performance compared to two-level inverters [9], the NPC type is practically for most multilevel inverter topology used for driving induction motor. The proposed Neuro-Fuzzy controller in this current study, is adapted by a hybrid learning algorithm, for having optimum values of the control.

II.

Fig. 1. Three-level NPC inverter

There are four different kinds of vectors: Zero vectors: V0, V7, and V14. Large vectors: V15, V16, V17, V18, V19, V20. Medium vectors: V21, V22, V23, V24, V24, V25, V26. Small vectors: V1, V2, V3, V4, V5, V6, V8, V9, V10, V11, V12, V13. The result of plotting each of the output voltages in the d,q reference frame is shown in Fig. 2.

The Three Level Neutral Point Clamped (NPC) Inverter

Different circuit topologies have been implemented in multilevel inverters [35]-[38]. One of the most used is the Neutral Point Clamped (NPC) topology; obviously compared to conventional two-level inverter this structure is able of producing three different levels of output phase voltage. The three available states with a single leg are shown in Table I, and they are indicated by “1,” “0,” and “-1,” for “+U0/2,” “0,” and “ 0/2,"therefore practically each leg in the three-level inverter is constituted by four controllable switches with two clamping diodes [10]. Fig. 1, shows the circuit topology of a diode-clamped three-level inverter based on the six main switches(Sa1, Sa4, Sb1, Sb4, Sc1, Sc4) of the traditional two-level inverter, adding two auxiliary switches (Sa2, Sa3, Sb2,Sb3, Sc2, Sc3) and two neutral clamped diodes on each bridge arm respectively, the diodes are used to ensure the connection with the point of reference to obtain Midpoint voltages [11].With three possible output states, a total of 27 (33) switch combinations is possible, therefore, 19 different vectors are available in a three-level inverter since some of the combinations produce the same voltage vector [12]-[13].

Fig. 2. Space vector diagram for the three-level inverter

III. Induction Motor Modeling The dynamic model of an induction motor in the stationary reference frame can be written in d-q frame variables. The stator voltage vector Vs of the motor can be expressed as follows [17]:

TABLE I SWITCHING STATES OF THREE-LEVEL (NPC) INVERTER Sax Sa1 Sa2 Sa3 Sa4 Voltage output + +1 On On Off Off 2 0 On Off On Off 0 -1

Off

Off

On

On

2


=

+

(1)

=

+

(2)

=

+

(3)

International Review of Automatic Control, Vol. 9, N. 4

183


= +1

and its components can be

> +

written as: =0 =

+

(4)

=

+

(5)

=

+

(6)

and the rotor flux vector stator reference frame are:

< =

+

(7)

=

+

(8)

=

+

(9)

(11)

+

= +2 = +1 +2 =0 + = 1 = 2

IV. Direct Torque Control Principle

> +2 > > + > > > > 2 2 >

(12)

As introduced earlier, the Direct Torque Control method has become a most popular approach to control the dynamic performance parameters of IM drive [16]. This method is based on the control of torque and flux to desire magnitude, by selection of the appropriate voltage vector according to the pre-defined vector table [14]. The choice is usually based on the use of hysteresis regulators, whose function is to control the magnitude of stator flux and the electromagnetic torque. The magnitude of the developed torque is given by: =

3 22

|

||

|

(10)

Fig. 3. Torque and Flux hysteresis comparator

The stator flux can be evaluated by integrating from the stator voltage equation:

where = . It’s obvious that the induction motor electromagnetic torque is determined by the increasing of the cross of the stator flux and the rotor flux, the amplitude of the stator flux is a constant value, but the rotor flux value is determined by the value of applied load, also, the stator flux changes fast if it compared with the rotor flux, finally the developed torque can be varied by the stator flux and the angle , and is determined by combining the stator flux and the rotor flux angle. The creation of the stator flux is givenby the addition of active vectors and remainsin stop positionby the addition of zeros vectors.

V.

= The flux angle

(

)

(13)

is calculated as: =

(14)

Fig. 4 demonstrates the repartition of the flux under the plan , where is divided into twelve sectors, where each sector has 30°, and the first sector is situated between -15°, 15°. Depending on the stator flux position (sectors number) and the values of the outputs of torque and flux controllers, and respectively, the value of the optimal vector is selected. Several switching tables for the three-level inverter are presented in the literature [18], [19]. Table II gives the developed inverter selector, to realize an optimum control. The importance of the zero vectors selected is to reduce the commutations number of the inverter switches.

Direct Torque Control Using Three Level Inverter

The hysteresis band flux controller process, is the control of the flux error by comparing the desired flux and the actual flux and generating three levels of output , as flux increase (+1), unchanged (0) or decrease (-1).The band width of the controller equals :



184


A PID controller calculates an "error" value as the difference between a measured process variable and a desired set point. The Ziegler-Nichols tuning method is a classic technique used to tune the value of controller gains PID. The proportional, integral and derivative terms are summed to calculate the output of this controller. Defining u(t) as the controller output, the final form of the PID algorithm is [20]: ( )=

H

+1

0

-1

H +2 +1 0 -1 -2 +2 +1 0 -1 -2 +2 +1 0 -1 -2

01 21 21 26 26 22 22 25 25 17 03 05 19

09 25 25

10 20 06

11 26 26

12 15 01

24 24 26 26

05 19 15 01

25 25 21 21

06 20 16 02

23 23 15 01

04 18 21 21

24 24 16 02

05 19 22 22

03 17

23 23

04 18

24 24

( )

+

( )

(15)

Fig. 6 shows the basic structure of a PID controller, the above schemes about DTC have generally a PID or PI controllers for speed control, because these controllers ensure a control of the motor speed and drive it perfectly to the reference value, but in this case, they proffers a low performance for solving the problem of the high ripples torque, therefore, the Neuro-fuzzy speed controller is proposed to replace them.

Fig. 4. Sectors of the Stators magnetic flux TABLE II SWITCHING TABLE SELECTOR Sectors 02 03 04 05 06 07 08 16 22 17 23 18 24 19 2 22 03 23 04 24 05 Zero vector 01 21 02 22 03 23 04 15 21 16 22 17 23 18 17 23 18 24 19 25 20 03 23 04 24 05 25 06 Zero vector 06 26 01 21 02 22 03 20 26 15 21 16 22 17 23 18 24 19 25 20 26 23 04 24 05 25 06 26 Zero vector 25 06 26 01 21 02 22 25 20 26 15 21 16 22

( )+

For testing the different performances of direct torque control in this study, a switch selector is placed for the purpose to allow a choice between the conventional PID speed regulator and the proposed speed regulator which based on the method of Neuro-fuzzy(Fig. 5).

Fig. 6. Scheme of a PID algorithm

VII. DTC with NEURO-FUZZY Controller Fuzzy logic and neural networks are complementary technologies in the design of intelligent systems. Artificial neural networks are low level computational algorithms that offer good performance with sensory data, while fuzzy logic deals with reasoning in a higher level than ANN [9]. The properties of both FL and ANN are combined to produce the ANFIS controller [21]. The proposed scheme utilizes Sugeno-type Fuzzy Inference System, with the parameters inside the FIS determined by a neuralnetwork back propagation method. The ANFIS is designed by taking the speed error ( ( )) and the change ( ) in speed error as the inputs. The output stabilizing signal is computed using the Fuzzy membership functions depending on these variables [22]. The ANFIS structure is composed by five layers (Figure 7) [23], where the first layer represents the inputs, the second layer represents the fuzzification, the third and fourth layer represent the fuzzy rules evaluations, and last layer represents the defuzzification [26]. The depicted model defines a controller with two inputs and one output. Each input has a many membership functions.

Fig. 5. Scheme of the proposed DTC

VI. DTC with PID Controller Due to their simple structure and robust performance, the PID controllers are the most commonly used feedback controllers in industrial process such as variable speed drive. Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved


185


where wi is normalized firing strength from layer 3 and (pi; qi; ri) is the consequent parameter set of this node. Layer 05: A single fixed node is in this layer labeled Sum which computes the overall output as the summation of incoming signals: =

.

=

. (22)

The inputoutput data set has been taken from a PID controller tuned using conventional method. The proposed approach has been implemented using ANFIS editor of MATLAB as shown in Fig. 8.

Fig. 7. ANFIS structure for Takagi and Sugeno's system with two inputs-one output

The rule base is assumed to contain the fuzzy if-then rules of a Takagi and Sugeno's type: R1: If x is A1and y is B1Then y1 = f1(x,y) = p1x +q1y + r1

(16)

R2: Ifx is A2and y is B2Then y2 = f2(x,y) = p2x +q2y + r2

(17)

The structures of the ANFIS and the layers are explained briefly as follows [26]: Layer 01: Every node i in this layer is an adaptive node with node function: = =

( ) for = 1,2or ( ) for = 3,4

Fig. 8. Matlab ANFIS Editor

The procedure to get an ANFIS controller is as follow: i. Load data. ii. Select the number and shape of membership function for each input. iii. Bound the error tolerance and number of Epochs. iv. Start the training. The training process stops whenever the designated epoch number is reached or the training error goal is achieved. A combination of such intelligent systems, like ANFIS provides even better results than just neural networks or fuzzy control [24].The structure of the proposed controller is shown in Fig. 9, it’s composed by tow inputs and a single output [27], the first input is the error of speed and is represented with five Gaussian membership functions (Fig. 10); the second is the change in error speed and is represented with ten Gaussian membership functions (Fig. 11). The output represents the control of the torque (Fig. 12). A surface representation of this controller is given in Fig. 13. The rules used are equal to 5×10= 50 rules.

(18)

where x (or y) is the input node i and Ai or Bi-2 is a linguistic label associated with this node. Therefore O1i is the membership grade of a fuzzy set (A1; A2; B1; B2). Layer 02: Each node in this layer is a fixed node labeled prod whose output is the product of all incoming signals: =

( ) ·

=

( ) , = 1,2

(19)

Each node output represents the firing strength of a rule. Layer 03: Each node in this layer is a fixed node labeled Norm whose outputs are normalized firing strength given by: =

=

+

, = 1,2

(20)

VIII. Simulation Results and Discussion

Layer 04: Every node in this layer is an adaptive node with a node function given by: =

.

=

(

+

+ )

Modeling and simulation have been performed to examine the control algorithm of DTC multi-levels with PID and ANFIS controllers using MATLAB/SIMULINK software.

(21)



186


The load equal 05[N.m] is applied to this system att=0.5 [s]. The parameters of the induction motor prototype are mentioned in Appendix I. 20

Te with PID Controller Te with Neuro-Fuzzy Controller Te*

15 10 5

6

0 5

-5

Fig. 9. Structure of the proposedANFIS controller

-10

4 0.65

0

0.2

0.4 0.6 Time [s]

0.7

0.75

0.8

1

Fig. 14. The Torque responses in DTC withPID andNeuro-Fuzzy Controllers TABLE III COMPARATIVE ANALYSIS OF TORQUE RIPPLE Torque Ripple in Torque Ripple in DTC Multi-Levels Using a DTC Multi-Levels Using a conventional PID Controller Neuro-Fuzzy Controller 0.6 0.9 [N m] 0.2 0.4 [N m]

Fig. 10. Membership functions of the first input

30

Isa Isb Isc

20 10 0

Fig. 11. Membership functions of the second input 5

-10

0

-20 -30

-5 0.45

0

0.2

0.5

0.55

0.4 0.6 Time [s]

0.8

1

Fig. 15. Current responses in DTC with a PID Controller 30

Fig. 12. The output of the ANFIS controller

Isa Isb Isc

20 10 0 8 6 4 2

5

-10

5

-10

0

-20

DE

-30

0

-5 0 5 E

10

-5

Fig. 13. The nonlinear profile of the usedNeuro-Fuzzy controller

-5 0.45

0

0.2

0.5

0.55

0.4 0.6 Time [s]

0.8

1

Fig. 16. Current responses in DTC with Neuro-Fuzzy Controller



187


500 Selected signal : 50 cycles. FFT window (in red): 1 cycles 20 0 -20 0

0.2

0.4 0.6 Time (s)

0.8

1

0

Fundamental (50Hz) = 13.28 , THD= 44.26% 40 30 20 10

-500 -500

0

0

20

40 60 Harmonic order

80

0 Vs-d [V]

100

500

(a) 500

Fig. 17. Spectral responses analyze ofCurrent in DTC withPID Controller

Selected signal: 50 cycles. FFT window (in red): 1 cycles 20 0

0

-20 0

0.2

0.4 0.6 Time (s)

0.8

1

Fundamental (50Hz) = 16.56 , THD= 21.36% 25 20

-500 -500

15

0 Vs-d [V]

10

500

(b) 5 0

0

20

40 60 Harmonic order

80

Figs. 20. Supply responses of induction motor in(d-q) plan for DTC with PID (a) and Neuro-Fuzzy (b) Controllers

100

14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

Fig. 18. Spectral responses analyze of Current in DTC withNeuro-Fuzzy Controller 200

Wrwith with PID Wr PIDController Controller Wrwith with Neuro-Fuzzy Controller Wr Neuro-Fuzzy Controller Wr* Wr*

150 200

100

150 100

50

With PID Controller With Neuro-Fuzzy Controller

0.05

0.1

0.15 Time [s]

0.2

0.25

50 0

0

0.02

0.04

0.06

Fig. 21. The responses of Sectors Flux repartition in DTC with PID and Neuro-Fuzzy Controllers

0.08

0 0

0.2

0.4 0.6 Time [s]

0.8

Fig. 14 shows the responses of the torque in DTC multi-levels with using PID and Neuro-Fuzzy controllers. The controller which assisted with an artificial intelligence improves the system by reducing the high ripples in the electromagnetic torque, so it allows to avoid an important system vibration.

1

Fig. 19. Rotor Speed responses in DTC with a PID andNeuro-Fuzzy Controllers



188


1.5

Firstly, the mathematical model of the induction motor is analyzed in terms of stator voltage and torque equations. Next, the control signal is generated by the PID controller, to drive the motor at its speed reference, then; it produces the reference electromagnetic torque. After that, the performances of speed and the torque are compared with those obtained by using an ANFIS controller. The results of numerical simulations show that the speed responses in both used controllers are similar, but in ANFIS controller it has been observed that the torque ripples is significantly reduced; another improvement observed with this controller, is the reduction in phase current distortion. The concluding analysis for this study shows that the proposed ANFIS speed control technique brings better performances than classical PID control technique in DTC control.

1

0.5

0

-0.5

-1

-1.5 -1.5

-1

-0.5

0 0.5 Flux-d [Wb]

1

1.5

(a) 1.5

Appendix

1

TABLE A1 I NDUCTION MOTOR PARAMETERS Number of pairs of poles 2 Rated power 1.5 kW Rated frequency 50 Hz Rated speed 1420 rpm Rated voltage 220/380 V Rated current 6,4/ 3,7A Stator resistance 4,85 Rotorresistanc 3,805 Stator inductance 274 mH Rotor inductance 274 mH Mutual inductance 258 mH Moment of inertia 0,031 kg m Viscous friction coefficient 0,0114 kg m /s

0.5

0

-0.5

-1

-1.5 -1.5

-1

-0.5

0 0.5 Flux-d [Wb]

1

1.5

(b)

TABLE A2 GAINS VALUES FOR PID CONTROLLER USED KP KI KD 7.35 2.71 0.43

Figs. 22. The responses of Stator flux trajectoryin (d-q) plan DTC with PID (a) and Neuro-Fuzzy (b) Controllers

By the comparison of Figs. 15, 16, 17 and 18 it can also be seen that the Neuro-Fuzzy system has a better response of the stator current due to the important decreasing in THD value. In Fig. 19, the time response of the rotor speed is very quick (0.04[s]), that is a major advantage in DTC control, both used controllers are robust against the applied disturbances, but generally this result has not a large signification between these controllers. The Fig. 20 illustrates the stator voltage in (d-q) plan, it can be noted that by using the Neuro-Fuzzy, the supply system has a more selection of voltage vectors when compared to the use of a PID controller. The Fig. 21represents the repartition of the stator flux sectors in (d-q) plan. The Fig. 22 comprises the Stator Flux trajectory in (d-q) plan; where in both responses, no ripples exist in the stator module.

IX.

References [1]

[2]

[3]

[4]

[5]

Conclusion

This paper discusses a comparative study of conventional PID and Adaptive Neuro-Fuzzy speed controllers applied in Direct Torque Control of induction motor, using a three-level voltage source inverter.

[6]

[7]


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[25]

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Authors’ information University of sciences and Technology Mohamed Boudiaf, (USTO), Oran, Algeria. M. L. Zegai was born in Oran Algeria in June, 25, 1984. He received the diploma ofElectrical Engineering on Automatic in 2008, from



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University of sciences and Technology Mohamed Boudiaf, (USTO), in 2012, he received his M.S degree in Electrical Engineering on control and analyze the electrical machines from the high school of technical teaching of Oran, Currently, he is a PHD Student at the (LDEE) laboratory of (USTO), Algeria. His research interests include Electrical machines and Drives Control, Power Electronics, as well as Intelligent Control. M. Bendjebbar was born on August, 16, 1965 in Relizane Algeria. He received his B.S degreein Electrical Engineering, M.S degree and Phd degrees, in 1989, 1993 and 2007 at the University of Sciences and Technology of Oran. He is currently Professor of Electrical Engineering at the University of Sciences and Technology of Oran. His research interests include Electrical machines and Drives Control, Power Electronics, as well as Intelligent Control and diagnostics. K. Belhadri was born in Oran Algeria in January, 31, 1985. She received the diploma of Electrical Engineering on Automatic in 2008, from University of sciences and Technology Mohamed Boudiaf, (USTO), in 2012, she received his M.S degree in Electrical Engineering on control and analyze the electrical machines from the high school of technical teaching of Oran, Currently, she is a PHD Student at the (LDEE) laboratory of (USTO), Algeria. Her research interests include nonlinear control, robotic and aerospace vehicles (X4-flyer vehicles) and intelligent Control. F. Lakhdari graduated at respectively the University of Sciences and technology (USTO) and the high school of technical teaching of Oran (Algeria). He obtains his doctorate degree in the field of power electronics and solar energy at the USTO University in 2008. His scientific research is focusing on electrical storage systems, static converters, and renewable energies.



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Automatic Control

Automatic Control

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