**7. References**


[4] Azcue P., J. & Ruppert, E. [2010]. Three-phase induction motor dtc-svm scheme with self-tuning pi-type fuzzy controller, *Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on*, Vol. 2, pp. 757 –762.

16 Will-be-set-by-IN-TECH

proposed in this chapter. All the test results showed the good performance of the proposed

This chapter presents the DTC-SVM scheme with T-S fuzzy controller for the three-phase IM. The conventional DTC-SVM scheme takes two PI controllers to generate the reference stator voltage vector. To improve the drawback of this conventional DTC-SVM scheme is proposed the Takagi-Sugeno fuzzy controller to substitute both PI controllers. The proposed controller calculates the quadrature components of the reference stator voltage vector in the stator flux reference frame. The rule base for the proposed controller is defined in function of the stator flux error and the electromagnetic torque error using trapezoidal and triangular membership functions. The direct component of the stator voltage takes a linear combination of its inputs as a consequent part of the rules, however, the quadrature component of the stator voltage takes the similar linear combination used in the first output but with the coefficients interchanged, not to be necessary another different coefficients values for this output. Constant switching frequency and low torque ripple are obtained using space vector

Simulations at different operating conditions have been carried out. The simulation results verify that the proposed DTC-SVM scheme with T-S fuzzy controller achieved good performance measures such as rise time, settling time and torque ripple as expected, It shown the fast torque response and low torque ripple in a wide range of operating conditions such as step change in the motor load, no-load sudden change in the speed reference, and the application of an arbitrary load torque profile. These results validate the proposed scheme.

[1] Abu-Rub, H., Guzinski, J., Krzeminski, Z. & Toliyat, H. [2004]. Advanced control of induction motor based on load angle estimation, *Industrial Electronics, IEEE Transactions*

URL: *http://www.mathworks.fr/matlabcentral/fileexchange/24403-modelamento-e-simulação-do-*

[3] Azcue P., J. L. [2010]. *Three-phase induction motor direct torque control using self-tuning pi-type type fuzzy controller.*, Master's thesis, University of Campinas (UNICAMP).

[2] Azcue P., J. L. [2009]. Modelamento e simulação do motor de indução trifásico.

URL: *http://cutter.unicamp.br/document/?code=000777279*

DTC-SVM scheme with T-S fuzzy controller.

**6. Conclusion**

modulation technique.

**Author details**

Alfeu J. Sguarezi Filho

*on* 51(1): 5 – 14.

*motor-de-indução-trifásico*

**7. References**

José Luis Azcue and Ernesto Ruppert *University of Campinas (UNICAMP), Brazil*

*CECS/UFABC, Santo André - SP, Brazil*


[20] Park, Y.-M., Moon, U.-C. & Lee, K. [1995]. A self-organizing fuzzy logic controller for dynamic systems using a fuzzy auto-regressive moving average (farma) model, *Fuzzy Systems, IEEE Transactions on* 3(1): 75 –82.

**Industrial Application of a Second Order Sliding**

**Chapter 15**

**Mode Observer for Speed and Flux Estimation in**

Recently, considerable research efforts are focused on the sensorless Induction Motors (*IM*) control problem. We refer the reader to [12] for a tutorial account on the topic. Indeed, industries concerned by sensorless *IM* drives are continuously seeking for cost reductions in their products. The main drawback of *IM* is the mechanical sensor. The use of such direct speed sensor induces additional electronics, extra wiring, extra space, frequent maintenance, careful mounting and default probability. Moreover, the sensor is vulnerable for electromagnetic noise in hostile environments and has a limited temperature range.

To avoid mechanical sensor (speed, position and load torque) of *IM*, several approaches for the so-called "sensorless control" have attracted a great deal of attention recently (see for example [21], [15], [22], [16], [11], [14], [6], [10], [1], [8], [19]. These methods can be classified into three

• Strategies based on IM spatial saliency methods with fundamental excitation and high

• Fundamental motor model strategies: adaptive observer [21], Luenberger observe [15], Kalman filter observer [11], high gain observer [14], [6], sliding mode observer [10], [1],

First and second strategies have been a subject of growing interest in recent years. For example the second strategy based on IM spatial saliency with extra converters is a robust and physical method. But artificial intelligence and spatial saliency algorithms are quite heavy for basic

> ©2012 Ghanes et al., licensee InTech. This is an open access chapter 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, provided the original work is properly

©2012 Ghanes et al., licensee InTech. This is a paper 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, provided the original work is properly cited.

This chapter belongs to the third strategy using mainly observer methods.

**Sensorless Induction Motor**

Sebastien Solvar, Malek Ghanes, Leonardo Amet, Jean-Pierre Barbot and Gaëtan Santomenna

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/52910

**1. Introduction**

main strategies.

microprocessors.

• Artificial intelligence strategies [22], [19].

frequency signal injection [16], [12].

interconnected high gain observer [8].

cited.

