hysteresis regulators. The first one is to **control** the **flux** and the other to **control** the **torque**. The use of **fuzzy** controllers permits a faster response and greater robustness. As an intelligent method, **fuzzy** **control** does not need an accurate mathematic model of the process to be controlled, and it uses the experience of people’s knowledge to form its **control** rule base. A **fuzzy** **logic** controller is used to select the voltage vectors in a conventional DTC in [7]. For the duty ratio **control** method, a **fuzzy** **logic** controller is used to determine the duration of the output voltage **vector** at each sampling period [8]. These **fuzzy** **logic** controllers can provide good dynamic **performance** and robustness. A **fuzzy** adaptive controller was also used to reduce **torque** ripples [9]. In this method the duty ratio of the vectors was calculated **based** on **fuzzy** estimators and can effectively reduce the **torque** ripples. However, it cannot have a constant frequency. A significant improvement in the steady state **performance** was reported. Some of the different solutions proposed include DTC with SVM, different power converter topologies, such as multi-level inverters [10], [11], a matrix converter [12], sensor-less methods [13], [14], optimum **stator** **flux** estimators for **high** speed operation [15], [16], and artificial intelligence techniques, such as **fuzzy** **logic** and neuro-controllers [17]. **Direct** **torque** **control** consists of a pair of hysteresis comparators, **torque** and **flux** calculators, a lookup table, and a voltage-source inverter (VSI) [18]. However, major problems usually associated with this drive are a switching frequency that varies with the operating conditions, a **high** **torque** and **flux** ripples with current distortion.

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Over the years **direct** **torque** **control** (DTC) of **induction** motor emerged as an alternative to field oriented **control** of **induction** motor [1]. DTC is employed for **high** **performance** and quick response drives [2].Compared to field oriented **control**, DTC has following advantages like (a) simple and quick response **control**, (b) absence of co-ordinate transformation and current controllers, (c) PI controllers in **Flux** and **torque** **control** loops[3-4]. The conventional DTC (CDTC) suffers **from** major disadvantages like (a) **high** **torque** and **flux** ripples, (b) accurate **estimation** of **torque** and **flux**, (c) sluggish speed response during low speed and sudden change in **torque** command and (d) variable switching frequency [3-5]. Over the last two decades several solutions are proposed by researches to improve CDTC. Few researches proposed improvements in CDTC by employing multilevel inverters [6-8], but multi level inverters results in **high** switching losses. Few developed DTC with variable gain hysteresis bands or by replacing hysteresis band with constant switching controllers [9-10]. CDTC is also improved by **space** **vector** **modulation** and discrete **space** **vector** **modulation** techniques as given in [11- 13] but in these methods accurate design of **torque** and **flux** loop PI controllers is required. The use of artificial intelligent techniques like neural networks, **fuzzy** **logic** for improvements in CDTC gained more importance in recent years. In order to improve CDTC few researchers did works on replacing conventional **torque** and speed PI controllers with **fuzzy**, neuro-**fuzzy** (ANFIS), sliding mode **fuzzy** and neural

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The **control** scheme for PMSM includes director **control** and **vector** **control** out of which **direct** **torque** **control** is popular. **Direct** **Torque** **Control** (DTC) method has been first proposed and applied for **induction** machines in the mid- 1980’s as reported in [1]. This concept can also be applied to synchronous drives [2]. Indeed, in the late 1990s, DTC techniques for the interior permanent magnet synchronous machine appeared, as reported in [3].Permanent magnet (PM) synchronous **motors** are widely used in **high**-**performance** drives such as industrial robots and machine tools to their advantages as: **high** efficiency, **high** power density, **high** **torque**/inertia ratio, and free maintenance. In recent years, the magnetic and thermal capabilities of the PM have been considerably increased by employing the **high** coercive PM material [4]. For some applications, the DTC becomes unusable, despite it significantly improves the dynamic **performance** of the drive compared to the **vector** **control** due to **torque** and **flux** ripples. Indeed, hysteresis controllers used in the conventional structure of the DTC generates a variable switching frequency, causing electromagnetic **torque** oscillations [5], this frequency is also varying with speed, load **torque** and hysteresis bands selected [6]. In addition, a **high** sampling frequency needed for digital implementation of hysteresis comparators and a current and **torque** distortion caused by sectors changes [7]. Several contributions have been proposed to overcome these problems, by **using** a multilevel inverter: more voltage **space** vectors available to **control** the **flux** and **torque**. However, more power switches are needed to achieve a lower ripple and almost fixed switching frequency, which increases the system cost and complexity [8]-[9]. In [10] and [11], two structures of **modified** DTC have been proposed to improve classical DTC performances by replacing the hysteresis controllers and the commutation table by a PI regulator, predictive controller and **Space** **Vector** **Modulation** (SVM). In this paper, a **modified** DTC **algorithm** with fixed switching frequency for PMSM is proposed to reduce the **flux** and **torque** ripples. It is an extension of the **modified** DTC scheme for the PMSM proposed by the authors in [12]. The **performance** of the basic DTC and the proposed DTC scheme is analyzed by modeling and simulation **using** MATLAB.

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Abstract — **Direct** **torque** **control** (DTC) is a new method of **induction** motor **control**. The key issue of the DTC is the strategy of selecting proper **stator** voltage vectors to force **stator** **flux** and developed **torque** within a prescribed band. Due to the nature of hysteresis **control** adopted in DTC, there is no difference in **control** action between a larger **torque** error and a small one. It is better to divide the **torque** error into different intervals and give different **control** voltages for each of them. To deal with this issue a **fuzzy** controller has been introduced. But, because the number of rules is too **high** some problems arise and the speed of **fuzzy** reasoning will be affected. In this paper, a comparison between a new **fuzzy** **direct**-**torque** **control** (DTFC) with **space** **vector** **modulation** (SVM) is made. The principle and a tuning procedure of the **fuzzy** **direct** **torque** **control** scheme are discussed. The simulation results, which illustrate the **performance** of the proposed **control** scheme in comparison with the **fuzzy** hysteresis connected of DTC scheme are given.

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By **using** an input-output feedback linearization **control**, the inverter reference voltage is obtained. Also a full-order adaptive **stator** flu x observer is designed and a new speed adaptive law is given. Thus the stability of the observer system is ensured [6]. S. A. Zaid [7] suggested a decoupled **control** of amplitude and **stator** **flux** angle to generate the pulses of voltage source inverter. MATLAB/SIMULINK software simulates the suggested and conventional DTC. The use of SVM enables fast speed and **torque** responses. Variations of motor parameter do not affect the optimization in the new method. M. sathish Kumar presents the comparative evaluation of the two popular **control** strategies for **induction** motor drive. These strategies are classical DTC and DTC-SVM. The Simu-link mode l of both classical and SVPWM **direct** **torque** **control** drives are simulated in all the four quadrant of operation) and the results are analyzed [8].A LNASIR Z. A. presents the design of a **direct** **torque** **control** model and tested **using** MATLAB/SIMULINK package. Simulation results illustrate the validity & **high** accuracy of the proposed model [9]. A new **torque** ripple reduction scheme is proposed with a **modified** look up table. This table including a large no. of synthesized non - zero active voltage **vector** to overcome the limitation of the conventional strategy and duty ratio **control** switching strategy [10]. The DT C principle is **based** upon the decoupling of **torque** and **stator** flu x. **direct** **torque** **control** method employees hysteresis comparator which produces **high** ripples in **torque** and switching frequency is variable. The proposed DTC- SVM scheme reduces **torque** ripples and preserves the DTC transient merits. The SVM technique is utilized to obtain the required voltage **space** **vector** which compensates the flu x and **torque** errors, at each cycle period [11] [12].

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The **induction** motor is most widely because of its **high** reliability, robust in operations, relatively low cost and modest maintenance requirements. But they require much more complex methods of **control**, more expensive and higher rated power converters than DC and permanent magnet machines. Three phase **induction** motor is widely used in industrial drive because they are reliable and rugged. Single phase **induction** **motors** are widely used for heavier loads for example in fans in household appliances. The fix speed service, **induction** **motors** are being increased with variable frequency drives. **Induction** motor achieves a quick **torque** response, and has been applied in various industrial applications instead of dc **motors**. It permits independent **control** of the **torque** and **flux** by decoupling the **stator** current into two orthogonal components FOC (Field Oriented **Control**). However it is very sensitive to **flux**, which is mainly affected by parameter variations. It depends on accurate parameter identification to achieve the expected **performance**. The **vector** **control** of IM drive for speed **control** is mainly classified into two types such as field oriented **control** (FOC) and **direct** **torque** **control** (DTC). In FOC, the speed of the **induction** motor is controlled like a separately excited dc-motor with more transformations and complexity involved in the system. In order to **control** the **induction** motor speed in simple way without required any transformations the DTC is used. In the middle of 1980 **direct** **torque** **control** was developed by Takahashi and Depenbrock as an alternative to field oriented **control** to overcome its problems. **Direct** **torque** **control** is derived **from** the fact that on the basis of the errors between the reference and the estimated values of **torque** and **flux** it is possible to directly **control** the inverter states in order to reduce the **torque** and **flux** errors within the prefixed band limits. **Direct** **torque** **control** is a strategy research for **induction** motor speed adjustment feeding by variable frequency converter. It controls **torque** on the base of keeping the **flux** value invariable by choosing voltage **space** **vector**.

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Takahashi and Noguchi. **Direct** **torque** **control** (DTC)drives are finding great interest, since ABB recently introduced the first industrial **direct**-**torque**- controlled **induction** motordrive in the mid-1980’s, which can work even at zero speed. This is a very significant industrial contribution.Conventional **direct** **torque** controlled **induction** **motors** are utilized hysteresis controller to compensate the **flux** and torqueerrors. Due to the use of flu x and **torque** hysteresis controller, conventional DTC suffers fro m **high** **torque** ripples and alsoswitching frequency is variable. To overco me the disadvantages of conventional DTC, several techniques have beendeveloped. One of them is the **direct** **torque** **control** **using** **space** **vector** **modulation** (DTC-SVM ). **Space** **vector** **modulation** isan **algorithm** wh ich is used to calculate the required voltage **space** **vector** to compensate the flu x and **torque** ripples. SVMtechnique is **based** on the switching between two adjacent boundaries of a zero **vector** and active vectors. SVM techniqueshave several advantages such as, lower **torque** ripple, lower switching losses. Also lower Total Harmonic Distortion (THD)in the current, and easier to implement in the dig ital systems.In this paper, SVM-DTC technique with PIcontroller for **induction** machine drives is developed.Furthermore, a robust full-order speed adaptivestator flu x observer is designed for a speed sensorless DTC-SVM system and a speed-adaptive law isgiven. The observer gain matrix, which is obtainedby solving linear matrix inequality, can improve therobustness of the adaptive observer gain in [7]. Thestability of the speed adaptive **stator** flu x observer isalso guaranteed by the gain matrix in very lowspeed. The proposed **control** algorith ms are verifiedby extensive simulation results.

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The figure summarizes the essential features of DTC **control**. Basically, the **control** is accomplished by simple but advanced scalar **control** of **torque** and **stator** **flux** by hysteresis-band feedback loops. There is no feedback current **control** although current sensors are essential for protection. Note that no traditional SPWM or SVM technique is used as in other drives. The indirect PWM **control** is due to voltage **vector** selection **from** the look- up table to constrain the **flux** within the hysteresis band. Similar to hysteresis band (HB) current **control**, there will be ripple in current, **flux**, and **torque**. The current ripple will give additional harmonic loss, and **torque** ripple will try to induce speed ripple in a low inertia system. In recent years, the simple HB-**based** DTC **control** has been **modified** by **fuzzy** and neuro-**fuzzy** **control** in inner loops with SVM **control** of the inverter. Multiple inverter **vector** selection in SVM within a sample time smoothes current, **flux**, and **torque**. However, with the added complexity, the simplicity of DTC **control** is lost. DTC **control** can be applied to PM synchronous motor drives also.

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This paper describes a mix of **direct** **torque** **control** (DTC) and **space** **vector** **modulation** (SVM) for a customizable speed sensor less **induction** motor (IM) drive. The motor drive is provided by a two-level SVPWM inverter. The inverter reference voltage is gotten in view of information output criticism linearization **control**, utilizing the IM display in the **stator** – axes reference frame with **stator** current further more **flux** vectors segments as state factors. Additionally, a powerful full-arrange versatile **stator** **flux** observer is intended for a speed sensor less DTC-SVM system and another speed-versatile law is given. By outlining the observer pick up matrix in view of state criticism H_∞ **control** hypothesis, the strength and robustness of the observer systems is guaranteed. At last, the viability and validity of the proposed **control** approach is verified by simulation results. Keywords : **Direct** **Torque** **Control** (DTC), Speed Sensor less **Induction** Motor (IM) Drive, **Space** **Vector**

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DTC drive over the last decade becomes one possible alternative to the well-known **Vector** **Control** of **Induction** Machines. Its main characteristic is the good **performance**, obtaining results as good as the classical **vector** **control** but with several advantages **based** on its simpler structure and **control** diagram. DTC (**Direct** **Torque** **Control**) is characterized, as deduced **from** the name, by directly controlled **torque** and **flux** and indirectly controlled **stator** current and voltage. The DTC has some advantages in comparison with the conventional **vector**-controlled drives, like:-**Direct** **torque** **control** and **direct** **stator** **flux** **control**, Indirect **control** of **stator** currents and voltages, Approximately sinusoidal **stator** fluxes and **stator** currents, **High** dynamic **performance** even at locked rotor, Absences of co-ordinates transform, Absences of mechanical transducers, Current regulators, PWM pulse generation, PI **control** of **flux** and **torque** and co- ordinate transformation are not required, Very simple **control** scheme and low computation time, Reduced parameters sensitivity, Very good dynamic properties.Conventional DTC has also some disadvantages: Possible problems during starting and low speed operation, **high** requirements upon **flux** and **torque** **estimation**, Variable switching frequency, these are disadvantages that we want to remove by **using** **fuzzy** **logic**. In the following, we will describe the application of **fuzzy** **logic** in DTC **control**.

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The generalized predictive **control** (GPC) of Clarke (Papafotiou and Kley, 2009), is considered as the most popular method of prediction, especially for industrial processes. This resolution is not repeated each time there is an optimal **control** problem: "how to get **from** the current state to a goal of optimally satisfying constraints" (Rawlings and Mayne, 2009). For this, you must know at each iteration the system state **using** a numerical tool. Temporal representation of generalized predictive **control** is given by (Fig.2), where there are controls u(k) applied to the system for rallying around the set point w (k). Numerical model is obtained by a discretization of the continuous transfer function of the model which is used to calculate the predicted output of a

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The DTC of DMC is **based** on Table 1, and the **control** of input power factor for fixed that to one, a hysteresis com- parator is added to confirmed that, whatever is the sec- tor which the input voltage **vector** is in, the MC takes any time two switching configurations with several directions for each VSI output **vector** selection by the classical DTC, this directions allows the possibility to **control** the input power factor by applied one to increase the angle and the second to decrease. The all probability switching con- figuration of MC used in DTC it gives in the Table 2, and the **space** **vector** diagram of output voltage and input cur- rent has been shown in Fig. 4 [14, 17, 18].

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On the other hand, multi-level inverters have become a very attractive solution for **high** power application areas [16][17]. The three-level neutral point clamped (NPC) inverter is one of the most commonly used multi-level inverter topologies in **high** power ac drives. By comparing to the standard two-level inverter, the three-level inverter presents its superiority in terms of lower stress across the semiconductors, lower voltage distortion, less harmonic content and lower switching frequency [18].

is proposed for **induction** motor drives to ensure maximum efficiency operation for a given **torque** demand. The continuous needs of energy savings require higher efficient electrical drives which uses Adjustable Speed Drives (ASDs). Due to its ruggedness, simple technology, maintenance-freeness and low cost, the **Induction** Machine (IM) still represents the major energy consumer in ASDs. For applications that require **flux**-weakening, IMs provide a better solution for ASDs. The efficiency of IM drives can be improved by **flux** adaptation according to the load demand. The **flux** adaptation can be done through three categories of loss-minimizing strategies, implemented for scalar or **vector** **control** of **induction** motor drives. These loss minimizing strategies are 1) **control** of a single motor variable, such as the displacement power factor or the slip frequency 2) the search **control**, where the motor **flux** is iteratively adapted to minimize the input power and 3) loss model of the motor and/or the power converter. The proposed strategy directly regulates the machine **stator** **flux** according to the desired **torque**, **using** an optimal **stator** **flux** reference. Therefore, the proposed strategy is suitable for motor **control** schemes that are **based** on **direct** **flux** regulation, such as **direct** **torque** **control** or **direct** **flux** **vector** **control**. The maximum efficiency per **torque** (MEPT) **stator** **flux** map is computed offline **using** the traditional no-load and short-circuit tests’ data. This strategy makes the motor efficiency is significantly improved below rated **torque** compared to the constant rated **flux** operation An iron loss model **based** on the **stator** **flux** and frequency is also proposed for the calibration of the machine loss model and also for on-line monitoring of the iron losses during motor.

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in the **vector** **control** are compared with the respective and currents generated by transformation of phase current equation (6) with help of unit **vector** ( ). The respective error generate the voltage command signal ∗ and ∗ through P-I compensators and these voltage commands are then converted into and voltages, these voltages are given to the input of SVPWM. The outputs of the **Space** **vector** pulse width **modulation** are the signals that drive the inverter. Among various **modulation** techniques for inverter, SVPWM technique is an attractive technique which directly uses the **control** variable given by the **control** system and identifies each switching **vector** as a point in complex **space**. The current model generates the rotor **flux** position and is dependent on the rotor time constant ( = ). The proposed block diagram is shown in the fig.1.

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The motor speed can be controlled indirectly by controlling the **torque** with a **fuzzy** controller. **Fuzzy** **logic** is **based** on the theory of **fuzzy** sets developed by Zadeh [9]. This is an extension of the classical theory for the incorporation of **fuzzy** set. The proposed **fuzzy** controller has two inputs and one output as described in Figure 5.

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Four-Wheels-Drive (4WD) Electric Vehicle (EV) controlled with **Direct** **Torque** **Control** **based** **Space** **Vector** Modula- tion (DTC-SVM) is presented, where the electrical traction chain was well analyzed and studied **from** the lithium bat- tery, the buck boost to the mechanical load behavior. The speed of four wheels is calculated independently during the turning with the electronic differential system computations which distributes **torque** and power to each in-wheel motor according to the requirements, adapts the speed of each motor to the driving conditions. The basic idea of this work is to maintain the initial battery state of charge (SOC) equal to 70% and the prototype was tested in several topology condi- tions and under speed. The simulations carried in Matlab/Simulink verified the efficiency of the proposed DTC-SVM controller, and show that the system has more favorable dynamic **performance**. Results also indicate that this strategy can be successfully implemented into the traction drive of the modern 4WD electric vehicles.

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The fundamental idea of a **direct** **torque** **control** is a **based** on the switching tables with hysteresis of **torque** and **stator** **flux**. **Using** this method, for a the minimization of the commutations of the inverter switches, on the **torque**/**stator** **flux** decoupling, on the **control** of the PWM generator, DTC requires precise knowledge of the amplitude and angular position of the controlled **flux** with respect to the stationary **stator** axis in addition to the angular velocity for the **torque** **control** purpose [8, 13]. We don’t require the rotor position in order to choose the voltage **vector**. This particularity defines the DTC as an adapted **control** technique of AC machines [10-12].

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The **direct** **torque** and **flux** **control** for **induction** machine drives has been developed as **direct** **torque** **control** (DTC) in [1], and as **direct** self **control** (DSC) in [2]. Classic DTC [1] has several drawbacks: it exhibits large **torque**, **flux**, and current ripple, produces annoying acoustical noise, operates with nonzero steady-state **torque** error, has difficulties in controlling the **flux** at low speeds, and the switching frequency is variable and lower than the sampling frequency. Three classes of **modified** DTC schemes that deal with these problems and attempt to improve DTC behavior have evolved: (a) schemes that use improved comparators and switching tables, while the original topology is unchanged [3]–[4]; (b) solutions that implement the DTC concept my means of **space**-**vector** **modulation** (SVM) [5]–[7]; (c) **torque** and **flux** **control** systems that explicitly use the variable- structure **control** (VSC) approach [8]–[11].

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