In recent years the **control** of high-**performance** **induction** **motor** drives has received widespread research interests. It has been valued more not only because it is the most used **motor** in industries but also due to their varied modes of operation. Also it has good self-starting capability, simple, rugged structure, low cost and reliability etc. Main property that makes it more useful for industries is its low sensibility to disturbance and maintenance free operation. Despite of many advantages of **induction** **motor** there are some disadvantages also. Like it is not true constant speed **motor**, slip varies from less than 1% to more than 5%. Also it is not capable of providing variable speed operation. But as it is so useful for industries we have to find some solution to solve these limitations and the solution is speed controller, that can take necessary **control** action to provide the required speed. Not only speed, it can **control** various parameters of the **induction** machine such as flux, torque, voltage, stator current. Out of the several methods of speed **control** of an **induction** such as changing no of pole, rotor resistance **control**, stator voltage **control**, slip power recovery scheme and constant V/f **control**, the closed loop constant V/f speed **control** method is most popular method used for controlling speed. In this method, the V/f ratio is kept constant which in turn maintains the magnetizing flux constant that eliminates harmonic problem and also the maximum torque also does not change. So, it‟s a kind of complete utilization of the **motor**. And the controller used are conventional P-I controller, and **fuzzy** **logic** controller.

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For the **fuzzy** **logic** controller, the reference speed is set as a constant. Error block generate the output which is the error between actual speed and the set speed that is applied to one input of **fuzzy** controller and other to store the error in the memory to compute the change in error. Multiplexer combine both inputs and give it to **Fuzzy** **logic** controller. Real time scope is used observe the actual behavioral of the system. The instrument block is used to send the output of **Fuzzy** **Logic** Controller to PIC. The **Fuzzy** controller in this project is design using Mamdani method as a **Fuzzy** Inference Scheme (FIS). The real time **Fuzzy** **logic** controller response is shown in Figure 2.6.

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A systematic approach of achieving robust speed **control** of an **induction** **motor** **drive** by means of Takagi-Sugeno based **fuzzy** **control** strategy has been investigated in this paper. Simulink models were developed in Matlab 7 with the TS-based **fuzzy** controllers (hybrid controller) for the speed **control** of IM. The **control** strategy was also developed by writing a set of 49 **fuzzy** rules according to the TS **control** strategy. The main advantage of designing the TS based **fuzzy** coordination scheme to **control** the speed of the IM is to increase the dynamic **performance** & provide good stabilization. Simulations were run in Matlab 7 & the results were observed on the corresponding scopes. Graphs of speed, torque, stator current, flux, etc. vs. time were observed. The outputs take less time to stabilize, which can be observed from the simulation results. But, from the incorporation of the TS based **fuzzy** coordination system in loop with the plant gave better results there by stabilizing the plant very quickly. The developed **control** strategy is not only simple, reliable, and may be easy to implement in real time applications, but also cost-effective as when this **control** scheme is implemented in real time, the size of the controller will become very small. Collectively, these results show that the TSfuzzy controller provides faster settling times, has very good dynamic response & good stabilization.

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Simulink because S-function programming knowledge is required to access the model variables. Another approach is using the Simulink Power System Block set [3] that can be purchased with Simulink. This block set also makes use of S- functions and is not as easy to work with as rest of the Simulink blocks. Reference [4] refers to an implementation approach similar to the' one in this paper but fails to give any details. In this paper, a modular, easy to understand Simulink **induction** **motor** model is described. With the modular system, each block solves one of the model equations. Though **induction** motors have few advantageous characteristics, they also posse's nonlinear and time- varying dynamic interactions [5 6], Using conventional PI controller, it is very difficult and complex to design a high **performance** **induction** **motor** **drive** system. The **fuzzy** **logic** **control** (FLC) is attractive approach, which can accommodate **motor** parametric variations and difficulty in obtaining an accurate mathematical model of **induction** **motor** due to rotor parametric and load time constant variations. The FLC is a knowledge-based **control** that uses **fuzzy** set theory and **fuzzy** **logic** for knowledge representation [7]. This paper presents a **fuzzy** **logic** controller suitable for speed **control** of **induction** **motor** drives.

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ABSTRACT: This paper proposes the controlling of **Induction** **motor** drives.Because of low maintenance and robustness, **induction** motors have many applications in industries. Speed **control** of **induction** **motor** is more important to achieve maximum torque and efficiency. Various **control** techniques such as scalar **control**, vector **control**, Sensor-less **control** are used. These Schemes suffers from parameter sensitivity and limited **performance** at low speed of operation. Sensor-less **control** of **induction** **motor** **drive** using model reference adaptive system with PI controller as reference model will limit the **performance** at low speed of operation. In this thesis, a novel adaptation mechanism is proposed which replaces PI controller in MRAS adaptation mechanism by a **fuzzy** **logic** controller. This is applied to a vector controlled **drive** and experimentally verified. This makes the reference model free from pure integration and less sensitive to stator resistance variations. This improves the **performance** of MRAS based sensor-less drives at low speed of operation.

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Today’s modern society is totally automated with less human interventions. All predictable and routines jobs are assigned to a machine. The automated machine alone does if the parameters are defined. The **control** algorithms and estimation of **induction** **motor** drives had grown significantly over the past few years and the innovation has furthermost propelled lately. Employing **induction** magnetic **motor** was improved enormously because of some of their advantages such as robust construction, reliable and it is free from customary maintenance. The variable speed drives for cage type **induction** magnetic **motor** requires fast torque response along with wide operating speed range irrespective of the variations in load, thus giving more advanced methods of **control** so as to meet the real demand. The exacting vector manages in the **induction** engine electric disks is all around acknowledged procedure whenever excessive degrees of effectiveness in the system reply are required. While using technique in the vector manage procedure, a **induction** engine has become effortlessly handled such as a on their own thrilled DC engine with regard to high **performance** programs and will be offering an increased a higher level active effectiveness. In addition the actual active effectiveness is actually required with regard to outstanding effectiveness associated with electric disks. These AC drives prerequisites can be satisfied by the vector **control** framework.

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In order to understand and analyze vector **control**, the dynamic model of the **induction** **motor** is necessary. It has been found that the dynamic model equations developed on a rotating reference frame is easier to describe the characteristics of **induction** motors. Any method for speed prediction is based on a model of the **motor** and the **drive**. The best accuracy of prediction for an **induction** **motor** is needed. Today, there are many choices of modelling techniques. One of them is system identification where it identifies the behaviour of a given system by estimating the model from input and output data. The estimated model is useful to simulate and predict the behaviour of the system. Not limited to that, the fitted model can be employed to regulate the output of plant.

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Volts/Hertz **control** is a basic **control** method, providing a variable frequency **drive** for applications like fan and pump. It provides fair speed and starting torque, at a reasonable cost. Sensor less vector **control** provides better speed regulation and the ability to produce a high starting torque. Flux vector **control** provides more precise speed and torque **control** with dynamic response. Field Oriented **Control** drives provide the best speed and torque regulation available for AC motors [6]. It provides DC like **performance** for AC motors, and is well suited for typical DC applications.

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An inverter is an electric device that converts DC to AC, the converted AC can be at any required voltage and frequency with the use of switching device and **control** circuits. Solid state inverters have no moving parts and are used in a wide range and application, from small switching power supplies in computers, to large electric utility high voltage dc applications that transport bulk power. Inverters are commonly used to supply AC power from DC sources such as solar panel or batteries. Inverters are used in various applications such as **induction** **motor** drives, UPS, standby power supplies, **induction** heating etc. Normally they are used for high power applications.

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The inherent simple construction, ruggedness, wide speed range of operation, low cost, fault tolerant capability, easy cooling simple excitation, requirement of simple converter circuit, high torque volume ratio, high efficiency and suitability under harsh environments are some of the important advantageous features of switched reluctance machine. The simple construction of the doubly salient, singly excited switched reluctance machine is shown in fig 3.1 The physical appearance of a Switched Reluctance **motor** is similar to that of other rotating motors (AC and DC) **Induction** **Motor**, DC **motor** etc. The construction of SRM is shown in figure. It has doubly salient construction. Usually the number of stator and rotor poles is even.

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Dynamic **performance** improved from an IM is enabled due to the development of Vector **Control** analysis. Just like in a dc **motor**, the torque and ﬂux components can be controlled independently using vector **control** strategy [15]. In order to analyses vector **control**, we need to develop a dynamic model of the IM. This is done by converting the 3-ϕ quantities into 2-axes system called the d-axis and the q-axis. Such a conversion is called axes transformation. The d-q axes can be chosen to be stationary or rotating. Further, the rotating frame can either be the rotor oriented or magnetizing ﬂux oriented. However, synchronous reference frame in which the d-axis is aligned with the rotor ﬂux is found to be the most convenient from analysis point of view [8]. A major disadvantage of the per phase equivalent circuit analysis is that it is valid only if the three phase system is balanced.

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Abstract--This paper presents reduction of torque ripples in an **Induction** **Motor** with DTC by duty cycle controller. The numbers of vectors are increased beyond the available eight discrete voltage vectors are used in this paper without increasing the number of semiconductor switches in the inverter. To achieve swift response, less overshoot and precision speed **control** to provide to enclose torque speed characteristics, look-up table based online tuning PI controller is projected for outer speed **control** loop. This paper shows a new algorithm for optimized value of stator flux based on the maxim reference value of electromagnetic torque to operate in conjunction with duty ratio **control**. MATLAB-Simulink is used to observe the **performance** of proposed technique. The simulation results shows the improved results of the proposed technique compared over the existing methods.

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In the conventional DTC, hysteresis controllers are a two value bang bang controllers, which has the same outputs for both small and big torque errors. Therefore torque ripples are produced. The torque ripples can be minimized by dividing the torque errors into several intervals on which **control** action taken. In this paper **fuzzy** **logic** based direct torque **control** is proposed. Here two **fuzzy** **logic** controllers for both flux and torque are proposed along with space vector modulation. The **fuzzy** controllers allow faster response and SVM technique provide a constant inverter switching frequency so small torque ripples and current distortion.

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In this paper, speed **control** and torque ripple minimization of a sensorless Permanent Magnet Brushless DC (PMBLDC) **motor** **drive** system with varying load is analyzed. Constant speed operation with minimum torque ripple during transient state is the most difficult part in the **drive** system. At starting condition, if the **motor** is started with constant DC source, the current is too high due to the absence of back EMF. Therefore the **motor** will start with high torque ripples. In order to eliminate the torque ripples during starting condition by limiting the starting speed of the **motor** with properly designed speed controller and varying DC source from zero to its rated voltage, this will improve the reliability of PMBLDC **motor**. Here, the speed **control** and torque ripple minimization of a sensorless PMBLDC **motor** during starting and running condition with conventional and **fuzzy** **logic** controllers are proposed. The **performance** parameters of a PMBLDC **motor** with these controllers are analyzed through MATLAB/Simulink software.

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The development of Inverter **Fuzzy** **Logic** **Control** for **Induction** **Motor** **Control** by Vector **Control** Method in Electric Vehicle. In response to concerns about energy cost, energy dependence, and environmental damage, a rekindling of interest in electric vehicles (EV’s) has been obvious. Thus, the development of power electronics technology for EV’s will take an accelerated pace to fulfill the market needs, regarding with the problem in this paper is presented development of **fuzzy** **logic** inverter in **induction** **motor** **control** for electric vehicle propulsion. The **Fuzzy** **logic** inverter is developed in this system to directed toward developing an improved propulsion system for electric vehicles applications, the **fuzzy** **logic** controller is used for switching process. This paper is describes the design concepts, configuration, controller for inverter **fuzzy** **logic** and **drive** system is developed for this high-**performance** electric vehicle.

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Software computing techniques such as **fuzzy** **logic** or **fuzzy** **control** (FC) provide a schematic method to incorporate human knowledge in the controller [8], it is a **control** algorithm depends on linguistic **control** strategy, which is proposed to employ the human experience knowledge to an automatic **control** strategy. While on the other hand, other **control** systems employed difficult arithmetic calculation to provide a model of the controlled plant, it only employs simple arithmetic calculation to model this experience. For The good **performance** of this **control** strategy, it can be or will be one of the best available answers for a broad class of challenging **control** problems [9].

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The DTC scheme is very simple in function; in its basic configuration it consists of hysteresis controllers, torque and flux estimator and a switching table. The basic concept of DTC is to **control** directly both the stator flux linkage (or rotor flux linkage, or magnetizing flux linkage) and electromagnetic torque of machine simultaneously by the selection of optimum inverter switching modes. The use of a switching table for voltage vector selection provides fast torque response, low inverter switching frequency and low harmonic losses without the complex field orientation by restricting the flux and torque errors within respective flux and torque hysteresis bands with the optimum selection being made. The DTC controller consists of two hysteresis comparator (flux and torque) to select the switching voltage vector in order to maintain flux and torque between upper and lower limit.The DTC scheme of **induction** **motor** **drive** is explained in detail.

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