**9. Constant switching frequency direct torque control using SVM**

The conventional direct torque control has several disadvantages, among which the variable switching frequency and the high level of ripples. Consequently, they lead to high-current harmonics and an acoustical noise and they degrade the control performance especially at low speed values. The ripples are affected proportionally by the width of the hysteresis band. However, even with choosing a reduced bandwidth values, the ripples are still important due to the discrete nature of the hysteresis controllers. Moreover, the very small values of bandwidths increase inverter switching frequency. In order to overcome these drawbacks, most of the studies presented in the literature have been oriented towards modification in the conventional DTC method by the introduction of a vector modulator [21, 22]. The vector PWM technique (SVM) is used to apply a voltage vector with a fixed switching frequency. The control system consists of replacing the switching table and the hysteresis comparators with proportional and integrating controllers (PI) for controlling the stator flux and the electromagnetic torque, [6, 23–27]. The main drawbacks of DTC-SVM using PI controllers are the sensitivity of the performances to the system-parameter variations and the inadequate rejection of external disturbances and load changes [28, 29]. To cope with this disadvantage, it is suggested to replace the conventional regulators used for the speed control, flux, and electromagnetic torque by intelligent controllers by adaptive fuzzy-PI and fuzzy logic to make the control more robust against the disturbances of the parameters of the machine.

#### **9.1 Space vector modulation algorithm**

This technique is much requested in the field of control in that the reference voltages are given by a global control vector approximated over a modulation period *Tz*. The principle of SVM is the prediction of inverter voltage vector by the projection of the reference vector *V* <sup>∗</sup> *<sup>s</sup>* between adjacent vectors corresponding to two non-zero switching states. For two-level inverters, the switching vector diagram forms a hexagon divided into six sectors, each one is expanded by 60° as shown in **Figure 7**.

The application time for each vector can be obtained by vector calculations, and the rest of the time period will be spent by applying the null vector.

When the reference voltage is in sector 1 (**Figure 8**), it can be synthesized by using the vectors V1, V2, and V0 (zero vector).

The determination of times T1 and T2 corresponding to voltage vectors are obtained by simple projections (**Figure 9**).

$$T\_1 = \frac{\sqrt{\mathbf{6}} \cdot \mathbf{V}\_{sa} - \sqrt{\mathbf{2}} \cdot \mathbf{V}\_{s\boldsymbol{\beta}}}{2\mathbf{V}\_{dc}} T \tag{21}$$

$$T\_2 = \frac{\sqrt{2} \cdot V\_{s\beta}}{V\_{dc}} \cdot T \tag{22}$$

where Vdc is the DC bus voltage.

**8. Global scheme of conventional direct torque control**

*Direct Torque Control Strategies of Electrical Machines*

**Figure 5.**

**Figure 6.**

**24**

*Speed anti-windup PI controller.*

*Global control scheme of basic direct torque control.*

The global control scheme of conventional direct torque control strategy is shown in **Figure 5**. It is composed of speed regulation loop; the proportionalintegral (PI) controller is used for the regulation. It is performed by comparing the speed reference signal to the actual measured speed value. Then the comparison error becomes the input of the PI controller. The pole placement method is used to determine the controller gains. The used PI controller in our work in the outer speed loop is the anti-windup controller. It allows to enhance speed control performance by canceling the windup phenomenon which is caused by the saturation of the pure integrator [20]. **Figure 6** shows the speed anti-windup PI controller diagram block. This strategy consists on the correction of the integral action based on the difference between the control signal and the saturation limit. The difference value

**Figure 7.** *Diagram of voltage space vector.*

T1, T2, and T0 are the corresponding application times of the voltage vectors,

The complete block diagram DTC-SVM improvement of induction motor drive with fuzzy logic controller is shown in **Figure 11**. The practical difficulty with PI controllers has been addressed in the previous section. The PI controllers are being replaced by fuzzy logic controllers that generates the module and the voltage vector angle in order to bring the stator flux and the electromagnetic torque to references optimally; this vector is used by a PWM control vector to generate the pulses for the control of the switches of the inverter, and PI speed controller is replaced by the adaptive fuzzy-PI speed controller to offer a good insensitivity to parameter variations, to get better response in external disturbance rejection and

The position of the reference voltage vector with respect to the stator flux vector must be chosen so as to maintain the stator flux and the electromagnetic torque in an optimal error band around their reference value. The errors of torque and flux are multiplied by "scales factors" to obtain standardized sizes and functions. These

**Figure 10** shows the global block diagram of DTC with SVM.

*Direct Torque Control Strategies of Induction Machine: Comparative Studies*

*DOI: http://dx.doi.org/10.5772/intechopen.90199*

respectively. Tz is the sampling time.

fast dynamics.

**27**

**Figure 10.**

**9.2 Principle of fuzzy direct torque control**

*Global control scheme of SVM-direct torque control with PI controller.*

*9.2.1 Selecting the position of the voltage vector*

#### **Figure 8.**

*Reference vector as a combination of adjacent vectors at sector 1.*

**Figure 9.** *Switching times of sector 1.*

*Direct Torque Control Strategies of Induction Machine: Comparative Studies DOI: http://dx.doi.org/10.5772/intechopen.90199*

#### **Figure 10.**

**Figure 7.**

**Figure 8.**

**Figure 9.**

**26**

*Switching times of sector 1.*

*Reference vector as a combination of adjacent vectors at sector 1.*

*Diagram of voltage space vector.*

*Direct Torque Control Strategies of Electrical Machines*

*Global control scheme of SVM-direct torque control with PI controller.*

T1, T2, and T0 are the corresponding application times of the voltage vectors, respectively. Tz is the sampling time.

**Figure 10** shows the global block diagram of DTC with SVM.

#### **9.2 Principle of fuzzy direct torque control**

The complete block diagram DTC-SVM improvement of induction motor drive with fuzzy logic controller is shown in **Figure 11**. The practical difficulty with PI controllers has been addressed in the previous section. The PI controllers are being replaced by fuzzy logic controllers that generates the module and the voltage vector angle in order to bring the stator flux and the electromagnetic torque to references optimally; this vector is used by a PWM control vector to generate the pulses for the control of the switches of the inverter, and PI speed controller is replaced by the adaptive fuzzy-PI speed controller to offer a good insensitivity to parameter variations, to get better response in external disturbance rejection and fast dynamics.

#### *9.2.1 Selecting the position of the voltage vector*

The position of the reference voltage vector with respect to the stator flux vector must be chosen so as to maintain the stator flux and the electromagnetic torque in an optimal error band around their reference value. The errors of torque and flux are multiplied by "scales factors" to obtain standardized sizes and functions. These

*9.2.3 Selection of the voltage vector magnitude*

*DOI: http://dx.doi.org/10.5772/intechopen.90199*

using the following equations:

PI controller by a fuzzy system method:

malized value of the integral action (*k*<sup>0</sup>

The parameters *k*<sup>0</sup>

and **15**.

**29**

condition.

The normalization PI parameters are given by:

*k*0

*<sup>p</sup>* and *k*<sup>0</sup>

and the adjusted proportional and integral terms (*k*<sup>0</sup>

*k*0

lation *Vs* 

the following algorithm:

lowing equations [30–32]:

The voltage vector obtained from the characteristic comes to the vector modu-

Calculate the biphasic components of the desired voltage vector using the fol-

*Vs<sup>α</sup>* ¼ *Vs* cos ð Þ*θ Vs<sup>β</sup>* ¼ *Vs* sin ð Þ*θ*

• Calculation of the relative position of the clock (PRH) in the sampling time by

In what follows, we show the synthesis and description of the adaptation of the

The fuzzy inference mechanism adjusts the PI parameters and generates new parameters during the process control. It enlarges the operating area of the linear

The inputs of the fuzzy adapter are the error (*e*) and the derivative of error (Δ*e*);

PRH ¼ Rem tð Þ *=*Ts *=*Ts*:* (24)

*<sup>p</sup>* <sup>¼</sup> *kp* � *kp* min *<sup>=</sup> kp* max � *kp* min (25)

*<sup>i</sup>* ¼ ð Þ *ki* � *ki* min *=*ð Þ *ki* max � *ki* min (26)

*<sup>i</sup>* are determined by a set of fuzzy rules of the form:

*<sup>p</sup>* is *Ci*, and *k*<sup>0</sup>

*<sup>p</sup>* and *k*<sup>0</sup> *i*

• Get the switching vectors and their operating cycle. Then calculate the

*Direct Torque Control Strategies of Induction Machine: Comparative Studies*

• Calculation of the area where the desired voltage vector is.

*9.2.4 The speed control of the IM by an adaptive fuzzy-PI controller*

controller (PI) so that it also works with a nonlinear system [33, 34].

*i* ).

where *Ai*, *Bi*, *Ci***,** and *Di* are fuzzy sets on corresponding supporting sets. The associated fuzzy sets involved in the fuzzy control rules are defined as follows:

**PB** Positive big **NB** Negative big **B** Big **PM** Positive medium **NM** Negative medium **ZE** Zero **PS** Positive small **NS** Negative small **S** Small

The membership functions for the fuzzy sets corresponding to the error e and Δ*e*

By using the membership functions shown in **Figure 15**, we satisfy the following

the outputs are the normalized value of the proportional action (*k*<sup>0</sup>

If *e* is *Ai* and Δ*e* is *Bi*, then *k*<sup>0</sup>

operating cycle of the null switching vector ð Þ *t*<sup>0</sup> ¼ 1 � *t*<sup>1</sup> � *t*<sup>2</sup> .

¼ *f εTem* ð Þ which in turn generates the states, and switches using *Vα*, *V<sup>β</sup>*

(23)

*p*) and the nor-

*<sup>i</sup>* is *Di* (27)

) are defined in **Figures 14**

**Figure 11.** *Global control scheme of SVM-direct torque control with fuzzy logic controllers and adaptive fuzzy-PI speed controller.*

values are used by the fuzzification block to be transformed into fuzzy values. These are used by the block fuzzy control rules after defuzzification; the value of (ψ) which must be added to the angle of the stator flux [30–32] (**Figure 12**).

**Figure 12.** *Controller structure for estimating the angle (ψ).*

#### *9.2.2 Selection of the voltage vector magnitude*

The voltage vector module must be selected to minimize the error of torque and flux. A fuzzy logic controller is designed to generate the appropriate voltage vector magnitude (**Figure 13**).

**Figure 13.** *Controller structure for voltage vector module estimation.*

*Direct Torque Control Strategies of Induction Machine: Comparative Studies DOI: http://dx.doi.org/10.5772/intechopen.90199*

#### *9.2.3 Selection of the voltage vector magnitude*

The voltage vector obtained from the characteristic comes to the vector modulation *Vs* ¼ *f εTem* ð Þ which in turn generates the states, and switches using *Vα*, *V<sup>β</sup>* the following algorithm:

Calculate the biphasic components of the desired voltage vector using the following equations [30–32]:

$$\begin{cases} V\_{sa} = V\_s \cos\left(\theta\right) \\ V\_{s\theta} = V\_s \sin\left(\theta\right) \end{cases} \tag{23}$$


$$\text{PRH} = \text{Rem}(\text{t}/\text{Ts})/\text{Ts}.\tag{24}$$

#### *9.2.4 The speed control of the IM by an adaptive fuzzy-PI controller*

In what follows, we show the synthesis and description of the adaptation of the PI controller by a fuzzy system method:

The fuzzy inference mechanism adjusts the PI parameters and generates new parameters during the process control. It enlarges the operating area of the linear controller (PI) so that it also works with a nonlinear system [33, 34].

The inputs of the fuzzy adapter are the error (*e*) and the derivative of error (Δ*e*); the outputs are the normalized value of the proportional action (*k*<sup>0</sup> *p*) and the normalized value of the integral action (*k*<sup>0</sup> *i* ).

The normalization PI parameters are given by:

$$k\_p' = \left(k\_p - k\_{p\text{ min}}\right) / \left(k\_{p\text{ max}} - k\_{p\text{ min}}\right) \tag{25}$$

$$k\_i^{\prime} = (k\_i - k\_{i\text{ min}}) / (k\_{i\text{ max}} - k\_{i\text{ min}}) \tag{26}$$

The parameters *k*<sup>0</sup> *<sup>p</sup>* and *k*<sup>0</sup> *<sup>i</sup>* are determined by a set of fuzzy rules of the form:

$$\text{If } \mathbf{e} \text{ is } \mathbf{A}\_i \text{ and } \Delta \mathbf{e} \text{ is } \mathbf{B}\_i \text{, then } k\_p' \text{ is } \mathbf{C}\_i \text{, and } k\_i' \text{ is } \mathbf{D}\_i \tag{27}$$

where *Ai*, *Bi*, *Ci***,** and *Di* are fuzzy sets on corresponding supporting sets. The associated fuzzy sets involved in the fuzzy control rules are defined as follows:


The membership functions for the fuzzy sets corresponding to the error e and Δ*e* and the adjusted proportional and integral terms (*k*<sup>0</sup> *<sup>p</sup>* and *k*<sup>0</sup> *i* ) are defined in **Figures 14** and **15**.

By using the membership functions shown in **Figure 15**, we satisfy the following condition.

values are used by the fuzzification block to be transformed into fuzzy values. These are used by the block fuzzy control rules after defuzzification; the value of (ψ) which must be added to the angle of the stator flux [30–32] (**Figure 12**).

*Global control scheme of SVM-direct torque control with fuzzy logic controllers and adaptive fuzzy-PI speed controller.*

The voltage vector module must be selected to minimize the error of torque and flux. A fuzzy logic controller is designed to generate the appropriate voltage vector

*9.2.2 Selection of the voltage vector magnitude*

*Direct Torque Control Strategies of Electrical Machines*

*Controller structure for voltage vector module estimation.*

*Controller structure for estimating the angle (ψ).*

magnitude (**Figure 13**).

**Figure 11.**

**Figure 12.**

**Figure 13.**

**28**

**Figure 14.** *Membership functions e and Δ*e*.*

**Figure 15.** *Membership functions k*<sup>0</sup> *<sup>p</sup> and k*<sup>0</sup> *i.*

$$\sum\_{1}^{m} v\_{i} = \mathbf{1} \tag{28}$$

where *C* ¼ ½ � *c*<sup>1</sup> … *c*<sup>2</sup> is the vector containing the output fuzzy centers of the

**e**

**Δ***e* **NB NM NS ZE PS PM PB** NB B B B B B B B NMB S S S S S B NSB B SSSBB ZE B B B S B B B PS B B SSSBB PMB S S S S S B PB B B B B B B B

*Direct Torque Control Strategies of Induction Machine: Comparative Studies*

P<sup>2</sup>

*kp* <sup>¼</sup> *kp* max � *kp* min � �*k*<sup>0</sup>

*ki* ¼ ð Þ *ki* max � *ki* min *k*<sup>0</sup>

The DTC control algorithms have been simulated by MATLAB/Simulink software. A comparative study between the three strategies for the direct torque control (DTC) of induction motor (IM) is presented. The first method is a conventional direct torque control (C-DTC) where the torque and the flux are regulated by the hysteresis controllers. The second one is direct torque control by space vector modulation strategy (SVM-DTC) where the torque and flux are regulated by PI controllers. The third one is fuzzy SVM-DTC with adaptive fuzzy-PI speed controller where the torque and flux are regulated by fuzzy logic controllers is presented. The simulation has been conducted for a three-phase 1.5 kW squirrel-cage induction motor with characteristics given in the appendix. The starting up and the steady states of the controlled motor with load introduction are presented. For the classical DTC, the chosen bandwidths of the hysteresis controllers are �0.01 Wb for

This section presents the starting up state of the induction motor according to speed step reference of 1000 rpm. Then, a load of 10 Nm is suddenly applied

**Figures 16** and **17** show, respectively, rotor speed, torque, stator phase current

**Figures 16** and **17(a)** illustrate the comparison between speed responses of conventional DTC and SVM-DTC-PI, according to the speed reference step of 1000 rpm. The load disturbance has been introduced between (t = 1 s) and (t = 2 s). The results of **Figure 16** show that the conventional DTC technique gives a good dynamic at starting up. We can notice that the speed regulation loop rejects the applied load disturbance quickly. The SVM-DTC-PI in **Figure 17(a)** kept the same

and *υ<sup>i</sup>* represents the membership value of the output *k*<sup>0</sup>

*<sup>p</sup>* and *k*<sup>0</sup>

*i.*

of PI controller is calculated by the following equations:

*<sup>i</sup>*¼<sup>1</sup>*wi* is the firing strength vector,

*<sup>i</sup>* are obtained (**Tables 3** and **4**), the new parameters

*<sup>p</sup>* or *k*<sup>0</sup>

*<sup>p</sup>* þ *kp* min (30)

*<sup>i</sup>* þ *ki* min (31)

*<sup>i</sup>* to output fuzzy

membership functions, *W* ¼ ½ � *w*1⋯*w*<sup>2</sup> *=*

*DOI: http://dx.doi.org/10.5772/intechopen.90199*

Once the values of *k*<sup>0</sup>

*Fuzzy rules base for computing k*<sup>0</sup>

**10. Simulation results**

flux and �0.1 Nm for torque.

between (t = 1 s) and (t = 2 s).

**31**

*isa*, flux magnitude, and the circular trajectory.

set *i*.

**Table 4.**

The fuzzy outputs *k*<sup>0</sup> *<sup>p</sup>* and *k*<sup>0</sup> *<sup>i</sup>* can be calculated by the center of area defuzzification as:

$$\begin{bmatrix} c\_1 & \dots & c\_2 \end{bmatrix} \begin{bmatrix} w\_1 \\ \vdots \\ w\_i \end{bmatrix}$$

$$\begin{bmatrix} k'\_p, k'\_i \end{bmatrix} = \frac{\sum\_{i=1}^3 w\_i c\_i}{\sum\_{i=1}^3 w\_i} = \frac{\begin{bmatrix} w\_1 \\ w\_2 \end{bmatrix}}{\sum\_{i=1}^2 w\_i} = b^T W \tag{29}$$


**Table 3.** *Fuzzy rule base for computing k*<sup>0</sup>

*p.*

*Direct Torque Control Strategies of Induction Machine: Comparative Studies DOI: http://dx.doi.org/10.5772/intechopen.90199*


**Table 4.**

X*m* 1

¼

**Δ***e* **NB NM NS ZE PS PM PB** NB B B B B B B B NM S B B B B B S NS S S B B B S S ZE S S S B S S S PS S S B B B S S PM S B B B B B S PB B B B B B B B

*<sup>p</sup>* and *k*<sup>0</sup>

¼ P<sup>3</sup> *<sup>i</sup>*¼<sup>1</sup>*wici* P<sup>3</sup> *<sup>i</sup>*¼<sup>1</sup>*wi*

*p.*

*k*0 *<sup>p</sup>*, *k*<sup>0</sup> *i* h i

*<sup>p</sup> and k*<sup>0</sup> *i.*

*Direct Torque Control Strategies of Electrical Machines*

The fuzzy outputs *k*<sup>0</sup>

defuzzification as:

*Membership functions k*<sup>0</sup>

**Figure 14.**

**Figure 15.**

**Table 3.**

**30**

*Fuzzy rule base for computing k*<sup>0</sup>

*Membership functions e and Δ*e*.*

*υ<sup>i</sup>* ¼ 1 (28)

<sup>¼</sup> *<sup>υ</sup>TW* (29)

*<sup>i</sup>* can be calculated by the center of area

*w*1 ⋮ *w*2 3 7 5

2 6 4

½ � *c*<sup>1</sup> … *c*<sup>2</sup>

P<sup>2</sup> *<sup>i</sup>*¼<sup>1</sup>*wi*

**e**

*Fuzzy rules base for computing k*<sup>0</sup> *i.*

where *C* ¼ ½ � *c*<sup>1</sup> … *c*<sup>2</sup> is the vector containing the output fuzzy centers of the membership functions, *W* ¼ ½ � *w*1⋯*w*<sup>2</sup> *=* P<sup>2</sup> *<sup>i</sup>*¼<sup>1</sup>*wi* is the firing strength vector,

and *υ<sup>i</sup>* represents the membership value of the output *k*<sup>0</sup> *<sup>p</sup>* or *k*<sup>0</sup> *<sup>i</sup>* to output fuzzy set *i*.

Once the values of *k*<sup>0</sup> *<sup>p</sup>* and *k*<sup>0</sup> *<sup>i</sup>* are obtained (**Tables 3** and **4**), the new parameters of PI controller is calculated by the following equations:

$$k\_p = (k\_{p\max} - k\_{p\min})k\_p' + k\_{p\min} \tag{30}$$

$$k\_i = (k\_{i\text{ max}} - k\_{i\text{ min}})k\_i^{\prime} + k\_{i\text{ min}}\tag{31}$$

### **10. Simulation results**

The DTC control algorithms have been simulated by MATLAB/Simulink software. A comparative study between the three strategies for the direct torque control (DTC) of induction motor (IM) is presented. The first method is a conventional direct torque control (C-DTC) where the torque and the flux are regulated by the hysteresis controllers. The second one is direct torque control by space vector modulation strategy (SVM-DTC) where the torque and flux are regulated by PI controllers. The third one is fuzzy SVM-DTC with adaptive fuzzy-PI speed controller where the torque and flux are regulated by fuzzy logic controllers is presented. The simulation has been conducted for a three-phase 1.5 kW squirrel-cage induction motor with characteristics given in the appendix. The starting up and the steady states of the controlled motor with load introduction are presented. For the classical DTC, the chosen bandwidths of the hysteresis controllers are �0.01 Wb for flux and �0.1 Nm for torque.

This section presents the starting up state of the induction motor according to speed step reference of 1000 rpm. Then, a load of 10 Nm is suddenly applied between (t = 1 s) and (t = 2 s).

**Figures 16** and **17** show, respectively, rotor speed, torque, stator phase current *isa*, flux magnitude, and the circular trajectory.

**Figures 16** and **17(a)** illustrate the comparison between speed responses of conventional DTC and SVM-DTC-PI, according to the speed reference step of 1000 rpm. The load disturbance has been introduced between (t = 1 s) and (t = 2 s). The results of **Figure 16** show that the conventional DTC technique gives a good dynamic at starting up. We can notice that the speed regulation loop rejects the applied load disturbance quickly. The SVM-DTC-PI in **Figure 17(a)** kept the same

fast speed response of DTC strategy. Since the same PI speed controller is used for both schemes, there is no difference in the transient response.

Then, the results illustrate the torque responses with load application. The figures show that at the beginning the speed controller (PI anti-windup) operates the system at the physical limit. It can be seen clearly that the constant switching frequency-based DTC strategy in **Figure 17(a)** has a reducer ripples level owing to the use of SVM compared to the conventional DTC in **Figure 16**, where it is observed that the high torque ripples exceed the hysteresis boundary. Next, the

**Figure 17.**

**33**

*Simulation results of the SVM-DTC control applied to IM. (a) SVM-DTC-PI. (b) SVM-DTC- Fuzzy.*

*Direct Torque Control Strategies of Induction Machine: Comparative Studies*

*DOI: http://dx.doi.org/10.5772/intechopen.90199*

**Figure 16.** *Simulation results of the classical DTC control applied to IM.*

*Direct Torque Control Strategies of Induction Machine: Comparative Studies DOI: http://dx.doi.org/10.5772/intechopen.90199*

**Figure 17.** *Simulation results of the SVM-DTC control applied to IM. (a) SVM-DTC-PI. (b) SVM-DTC- Fuzzy.*

fast speed response of DTC strategy. Since the same PI speed controller is used for

Then, the results illustrate the torque responses with load application. The figures show that at the beginning the speed controller (PI anti-windup) operates the system at the physical limit. It can be seen clearly that the constant switching frequency-based DTC strategy in **Figure 17(a)** has a reducer ripples level owing to the use of SVM compared to the conventional DTC in **Figure 16**, where it is observed that the high torque ripples exceed the hysteresis boundary. Next, the

both schemes, there is no difference in the transient response.

*Direct Torque Control Strategies of Electrical Machines*

**Figure 16.**

**32**

*Simulation results of the classical DTC control applied to IM.*

stator phase current with zoom is presented. The conventional DTC in **Figure 16** shows a chopped sinusoid waveform of current which indicates a high harmonic level, while SVM-DTC in **Figure 17(a)** shows a smoother sinusoid waveform. After that, the results exhibit the magnitude of stator flux evolution and circular trajectory. It is clear that the flux ripples of the conventional DTC have exceeded the hysteresis boundary. The magnitude and the trajectory illustrate that the flux takes a few steps before reaching the reference value (1.2 Wb) at the starting stage due to the zone's changing.

The simulation in **Figure 17(b)** shows that the SVM-DTC-fuzzy has better performance than those obtained by both other DTC strategies (conventional and SVM-PI). There is an appreciable decrease in the start-up response time; we can notice that the speed regulation loop rejects the applied load disturbance very quickly which proves the performance of adaptive fuzzy-PI controller as well as a significant attenuation of the ripples of the torque and of the sinusoidal current without any ripple in the steady state.

#### **11. Conclusion**

The main objective of this chapter is the improvement of the performance of an induction motor drive controlled by DTC. The objective of this improvement is to minimize the ripples of the couple and the flux of the IM on the one hand and the decrease of the switching frequency of the inverter on the other hand. In this context, a comparative analysis between different DTC strategies has been presented. This chapter began by explaining the principle of the conventional DTC, SVM-DTC-PI, and SVM-DTC-fuzzy with adaptive Fuzzy-PI speed controller. The chapter presents later a discussion based on the simulation results presented in the same work. The synthesis of this simulation study reveals advantages of SVM-DTCfuzzy scheme compared to the two strategies: conventional DTC and SVM-DTC-PI. It has been observed by comparing the torque, speed, and stator flux characteristics that the method SVM-DTC-fuzzy is better. It is clear that the current is sinusoidal without any ripple in the steady state and torque ripples are reduced. In order to improve the SVM-DTC-fuzzy to have better performances, this method has been associated to the adaptive fuzzy-PI speed controller. This association makes the induction motor-based DTC perform more and more stable; there is an appreciable decrease in the start-up response time; we can notice that the speed regulation loop rejects the applied load disturbance very quickly.


**Author details**

**35**

Cherifi Djamila\* and Miloud Yahia

provided the original work is properly cited.

GACA Laboratory, University of Dr Moulay Tahar, Saida, Algeria

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

**Item Symbol Data** Rotor self-inductance Lr 274 mH Mutual inductance Lm 258 mH Moment of inertia J 0.031 kg m2 Friction coefficient F 0.00114 kg m<sup>2</sup>

*Direct Torque Control Strategies of Induction Machine: Comparative Studies*

*DOI: http://dx.doi.org/10.5772/intechopen.90199*

/s

\*Address all correspondence to: d\_cherifi@yahoo.fr
