The BLDC **motor** is a three phase synchronous **motor** with stator having three phase concentrated windings and a rotor having permanent magnets. The magnetic material for permanent magnet rotor can be chosen, depending on the magnetic field density required. The ferrite magnets are most commonly preferred, because of its low cost. BLDC **motor** does not require any mechanical brushes and commutator assembly; hence, wear and tear of the brushes, sparking issues among the commutator and brushes are eliminated. The BLDC **motor** is also referred as an electronically commutated **motor**, which will work with a three phase supply generated by an inverter unit. For controlling the three phase inverter bridge, rotor position signals from the Hall sensor [1] or **sensorless** back EMF zero crossing points [11] are utilized to determine the phase commutation in an inverter unit. The disadvantages of © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

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Abstract—This paper presents a power factor corrected (PFC) bridgeless (BL) buck–boost converter-fed brushless direct current (BLDC) **motor** **drive** as a cost-effective solution for low-power applications. An approach of speed control of the BLDC **motor** by controlling the dc link voltage of the voltage source inverter (VSI) is used with a single voltage sensor. This facilitates the operation of VSI at fundamental frequency switching by using the electronic commutation of the BLDC **motor** which offers reduced switching losses. A BL configuration of the buck–boost converter is proposed which offers the elimination of the diode bridge rectifier, thus reducing the conduction losses associated with it. A PFC BL buck–boost converter is designed to operate in discontinuous inductor current mode (DICM) to provide an inherent PFC at ac mains. . In this work, **conventional** PI and **fuzzy** **logic** **controllers** have been used for the speed control of BLDC **motor** **drive**. The **performance** of **conventional** PI and **Fuzzy** **controllers** are compared under variable reference speed and varying supply voltages with improved power quality at ac mains. The obtained power quality indices are within the acceptable limits of international power quality standards such as the IEC 61000-3-2. The **performance** of the proposed **drive** is simulated in MATLAB/Simulink environment

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Recently, substantial research efforts have also been devoted to intelligent **controllers** such as artificial neural networks (ANN) and **fuzzy** **logic** to deal with the problems of nonlinearity and uncertainty of **system** parameters. The fundamental characteristics of neural networks are: ability to produce good models of nonlinear systems; highly distributed and paralleled structure, which makes neural-based control schemes faster than traditional ones; simple implementation by software or hardware; and ability to learn and adapt to the behavior of any real process. On the other hand it was shown that **fuzzy** **controllers** are capable of improving the tracking **performance** under external disturbances, or when the IFO **drive** **system** experiences imperfect decoupling due to variations in the rotor time constant. Neural network and **fuzzy** **logic** are gaining potential as estimators and **controllers** for many industrial applications, due to the fact that they posses better tracking properties than **conventional** **controllers**.

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use of multi-variable control structure. Most of these **controllers** use mathematical models and are sensitive to parametric variations. Very few adaptive **controllers** have been practically employed in the control of electric drives due to their complexity and inferior **performance**. **Fuzzy** **controllers**[8-10] have proved to be successful in recent years. These **controllers** are inherently robust to load disturbances. Besides, **fuzzy** **logic** **controllers** can be easily implemented. The **drive** **system** considered here consists of **fuzzy** **logic** controller, **conventional** controller, **PMBLDC** **motor** and MOSFET based inverter. All these components are modelled and integrated for simulation. The simulation results shows **Fuzzy** **logic** controller has great improvement in both transient and steady state responses of the **drive**. Contrary to the PI controller, **Fuzzy** **logic** controller makes the **PMBLDC** **drive** more robust to load variations. The key feature of this scheme is to compensate the oscillations and harmonics in the response of the **PMBLDC** **motor**. Results of this scheme are compared based on transient analysis and **performance** measures such as IAE,ISE,THD and Three phase Instantaneous power. Fig1 describes the basic building blocks of the **PMBLDC** **motor**. Section I discusses the basis of three Phase inverter and mathematical modeling of **PMBLDC** **motor** and Inverter. Section II discusses simulation results of PI fed gate control **PMBLDC** **motor** **drive**. Section III presents the simulation results of **Fuzzy** control gate method fed **PMBLDC** **motor** **drive**. Conclusion and references are given at the end.

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Circuit model of closed loop controlled induction **motor** **drive** **system** is shown in fig. 5.1. The output AC voltage is sensed and compared with reference voltage. The error generated is given to the PI controller to regulate the output voltage with respect to input voltage. The speed response of the closed loop **system** is shown in fig. 5.3. It seems that speed of the **motor** increases and then reduces to set value. The Torque variation is shown in fig.8 Line voltage and current waveform are shown .This shows that closed loop **system** is able to regulate the speed.

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Abstract- In this paper Induction motors are the most important workhorse in industries and they are manu- factured in large numbers .The induction motors have mainly developed in constant speed **motor** drives for general purpose application. The **motor** **drive** **system** comprises a voltage source inverter-fed induction **motor** (VSIM): namely a three-phase voltage source inverter and the induction **motor**. The squirrel-cage induction **motor** voltage equations are based on an orthogonal d-q reference rotating frame where the coordinates rotate with the controlled source frequency. The paper presents a novel **fuzzy** **logic** controller for closed loop Volts/Hz induction **motor** **drive** **system**. **Fuzzy** **logic** is a part of artificial intelligence(AI), which is an important branch of computer science or computer engineering. The inputs to the **fuzzy** **logic** controller are the linguistic variables of speed error and change of speed error, while the output is change in switching control frequency of the voltage source inverter. In this paper a **comparison** between **fuzzy** **logic** controller and traditional PI **controllers** are presented. The results validate the robustness and effectiveness of the proposed **fuzzy** **logic** controller for high **performance** of induction **motor** **drive**. Simulink software that comes along with MATLAB was used to simulate the proposed model.

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An induction or asynchronous **motor** is an AC **motor** in which all electromagnetic energy is transferred by inductive coupling from a primary winding to a secondary winding, the two windings being separated by an air gap. In three-phase induction motors, that are inherently self-starting, energy transfer is usually from the stator to either a wound rotor or a short-circuited squirrel cage rotor. Three-phase cage rotor induction motors are widely used in industrial drives because they are rugged, reliable and economical. Single-phase induction motors are also used extensively for smaller loads. Although most AC motors have long been used in fixed-speed load **drive** service, they are increasingly being used in variable-frequency **drive** (VFD) service, variable-torque centrifugal fan, pump and compressor loads being by far the most important energy saving applications for VFD service. Squirrel cage induction motors are most commonly used in both fixed-speed and VFD applications. Usage of induction motors reminds us to develop a better control over it. This induction motors have the advantage of decoupling (separation) of the torque and flux control which makes high servo quality achievable. [17, 22, 30] Torque and flux parameters are responsible for generating rotating motion of rotor. These parameters are affected depending on the load disturbances. The decoupling control feature can be adversely affected by load torque disturbances and parameter variations in the **motor**. This instantly lowers the speed down compared to the desired speed so that the variable-speed tracking **performance** of an Induction **motor** is degraded. In order to attain the rated speed there are many **controllers** like **conventional** PI controller. A proportional-integral controller (PI controller) is a generic control loop feedback

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The interior permanent magnet synchronous **motor** (IPMSM) is arguably the best choice for high **performance** variable speed drives (HPVSD). But its precise speed and torque control appear to be a complex task for researchers due to nonlinear coupling among its winding currents and the rotor speed, as well as the nonlinearity present in the electromagnetic developed torque because of magnetic saturation of the rotor core. The most tangible option for researchers is to employ simple fixed gain PI, PID **controllers** which provide good steady state **performance** but suffer from poor dynamic **performance**, sensitivity to parameter variations and occasional instability. Meanwhile, the **conventional** adaptive **controllers** require complex circuitry for real-time implementation. Artificial intelligent **controllers** like **fuzzy**, neural network, neuro-**fuzzy** **controllers** are good options in this case because they are capable of handling nonlinear systems without much knowledge of the **system** model and also they yield better transient speed response. But most often, researchers overlook the possibility of simultaneous torque ripple control while achieving better dynamic speed response. So in this thesis, a new IPMSM **drive** has been proposed with a combined approach of optimizing torque ripple and achieving better dynamic speed **performance** over a wide speed range.

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This project presents a **performance** of the canonical switching cell (CSC) device fed brushless DC (BLDC) **motor** **drive** for power quality (PQ) improvement. The employment of CSC not solely controlled the DC link voltage however additionally create the inverter to control at low frequency in order that switching losses are reduced. Furthermore the utilization of front end CSC improves the power factor at AC mains. A design methodology is introduced that blends the classical **fuzzy** **logic** **controllers** in associate degree intelligent means and so a new intelligent controller has been achieved. Moreover, associate degree intelligent switching pattern is evoked on the mixing mechanism that produces a choice upon the priority of the two controller parts; particularly, the classical PI and also the **fuzzy** constituents. The simulations done on varied processes using the new **fuzzy** controller provides ‘better’ **system** responses in terms of transient and steady-state performances in **comparison** to the pure classical PI or the pure **fuzzy** controller applications. The **performance** graph has been plotted for the total harmonic distortion (THD) and also the power factor (PF). A front end Canonical switching cell device operating in Discontinuous inductor Current Mode (DICM) is planned for power factor correction operation at AC mains. **fuzzy** **logic** is introduced so as to suppress the chattering and enhancing the hardiness of the PFC control **system**. The **performance** has been evaluated with the help of Mat lab-Simulink.

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This paper presents two **controllers** for implementing the current multiplier approach over a wide range of speed control of a Brushless DC (BLDC) **Motor** **drive** **system** as a cost effective low-power solution. A CUK converter at the front end feeds the DC bus of the Voltage Source Inverter (VSI), where, closed loop duty ratio control of the converter results in variable DC bus voltage, enabling close matching of the reference speed setting. Two alternate controller configurations viz. PI controller and **fuzzy** controller are introduced for generating gate trigger signals for the power MOSFET switch of the converter. **Comparison** of **performance** covering a over a wide range of operating speed of the entire **system** using PI/**FUZZY** **controllers** is carried out by simulation in MATLAB/Simulink platform.

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ABSTRACT: Permanent Magnet Brushless Direct Current (**PMBLDC**) motors are widely accepted for their high efficiency, reliability, good dynamic response and low maintenance. This paper proposes an effective speed control of **PMBLDC** **motor** **drive** using **conventional** Cuk converter and bridgeless Cuk converter. Further a **performance** **comparison** is made using PI and **fuzzy** **controllers** for the speed control of **PMBLDC** **motor** **drive** in both the converters. The simulation and **performance** **comparison** is analysed using MATLAB/SIMULINK software.

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A single stage PFC control strategy of a VSI fed PMBLDCM **drive** using power quality converter using **conventional** PI controller and **fuzzy** **logic** controller has been validated. As a conclusion, the increasing demand for using **fuzzy** **logic** as a controller for BLDC permanent magnet **motor** in modern intelligent motion control of BLDC motors, simulation have provided a good dynamic **performance** of the **fuzzy** **logic** controller **system**. Be sides. The simulation model which is implemented in a modular manner under MATLAB environment allows dynamic characteristics such as phase currents, rotor speed, and mechanical torque to be effectively considered. Also, THD of **system** is reduced and power factor is improved. The result paired with Matlab/simulink is a good simulation tool for modeling and analyzing **fuzzy** **logic** controlled brushless DC **motor** drives.

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Brushless D.C (BLDC) synchronous motors have been used in various fields of industrial applications for their high power/weight, high torque, high efficiency, long operating life, noiseless operation, high speed ranges and ease of **drive** control [1]. Permanent Magnet Brushless DC (**PMBLDC**) **motor** is defined as a permanent magnet synchronous **motor** with a trapezoidal Back EMF waveform [2]. BLDC motors do not have brushes for commutation. They are electronically commutated [3]. For the variable speed applications of BLDC **motor**, Proportional, Integral and Derivative (PID) **motor** control is commonly used control [4].Because; it has simple design and ease of control. However, its **performance** depends on proportional, integral and derivative gains [5- 6]. When the operating condition changes, the re-tuning process of control gains is necessary for dynamically minimize the total controller error. The various algorithms are used to find optimal PID controller parameters such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) [7-10].Particle Swarm Optimization (PSO) and genetic algorithm (GA) is given based on population size, generation number, selection method, and crossover and mutation probabilities. There is no guarantee for finding optimal solutions for **controllers** within a finite amount of time. To overcome the problems in PID controller, **fuzzy** **logic** controller and hybrid **fuzzy** PID **controllers** can be designed for the speed control of BLDC **motor**. In this proposed research work, the speed control of BLDC **motor** was analyzed and its **performance** has been observed by using **fuzzy** **logic** controller and hybrid **fuzzy** PID [11- 13].The simulation results of two methods are studied and compared with **conventional** PI controller by using MATLAB/SIMULINK computational software. The simulation results of proposed **controllers** are used to show the abilities and shortcomings of **conventional** PI controller.

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Abstract- This paper presents the modeling, simulation, and speed control aspects of a 3-phase 6/4 Switched Reluctance **Motor** (SRM) drives, using hybrid Artificial Intelligence **Fuzzy** **Logic** Controller **system**. Also a speed control design for Switched Reluctance **Motor** **drive** based on **fuzzy** **logic** controller is suggested. The **fuzzy** controller is proposed in this paper as speed controller for SRM. The whole control mechanism consists of a detailed report about the steady state and transient analysis of Switched Reluctance **Motor**. The control design results are then validated in real-time by Simulink / Matlab software package. The main aim of this project is to control the speed of the Switched Reluctance **Motor** very effectively using **Fuzzy** **Logic** Controller. Though PI controller is more popular and widely used, **Fuzzy** is something which is more advanced and efficient when compared to other **conventional** **controllers**.

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The ripple contents of stator current, electromagnetic torque and rotor speed are minimized with FLC method. The advantages of **Fuzzy** **Logic** Controller is that it does not require any mathematical model and only based on the linguistic rules.The use of the d-q-0 reference frame for BLDCM is based on the fact that, in a three-phase Y-connected **motor** with non- sinusoidal air gap flux distribution, the d-q-0 transformation of the three line-to-line back EMF‟s results in the finding of the d- and q- components identical to those of three phase back EMF‟s transformation.

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Similarly, in many material handling systems, adjustable speed drives can increase **system** efficiency and improve **system** reliability. For example, in many conveyor systems, lines are controlled by energizing and de-energizing a series of motors. These frequent starts and shutdowns are tough on motors and line components because of repeated stresses from starting currents and acceleration and deceleration of mechanical components. Using variable speed drives can smooth out line motion for more efficient and effective operation. Some motors have inherent speed control capabilities. For example, dc motors have excellent speed and torque control characteristics and are often used when high torque at low speeds is required. The speed adjustments of dc motors can be as much as 20:1, and they can operate at 5% to 7% of the motor’s base speed (some can even operate at 0 rpm). Some ac motors can also be used in speed adjustment situations. Wound rotor motors can have speed ratios of as much as 20:1 by changing the resistance in the rotor circuit. Another common method of controlling speed is to use induction motors combined with VFDs. Induction motors are widely used in industrial applications because of their inherent advantages in terms of cost, reliability, availability, and low maintenance requirements. Mechanics and

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The electric **drive** **system** is a vital part to **drive** any **motor**. The electric **drive** **system** is used to control the position, speed and torque of the electric motors. Many works has been done on power converter topologies, control scheme of the electric **drive** systems and on the **motor** types in order to enhance and improve the **performance** of the electric motors so as to exactly perform and do what is required [1]. Induction Motors (IMs) are widely used in industrial, commercial and domestic applications as they are simple, rugged, low cost and easy to maintain. Since IMs demands well control performances: precise and quick torque and flux response, large torque at low speed, wide speed range, the **drive** control **system** is necessary for IMs [2].

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ABTRACT: Brushless Direct Current (BLDC) motors are widely used due to high reliability, simple frame, straight forward control, and low friction. BLDC **motor** has the advantage of high speed adjusting **performance** and power density. Speaking of the **motor** **drive**, the most important part is commutation control. On the other hand, they show a high torque ripple characteristics caused by nonideal commutation currents. This limits their application area especially for the low-voltage applications. In order to minimize torque ripple for the entire speed range, a comprehensive analysis of commutation torque ripple was made according to phase advancing(PA) commutation control method. This approach is based on the terminal voltage sensing and converting the voltages into d-q reference frame and the commutation signals are generated by comparing it with reference values. The gating signals are obtained by switching sequence of BLDC **motor** and it is done using **fuzzy** **logic** controller(FLC).The design analysis and simulation of the proposed **system** is done using MATLAB version 2013a and the simulation results of proportional- integral (PI) controller and **fuzzy** **logic** controller(FLC) method is compared.

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Initially results are presented which demonstrate that the ANN can estimate the derivative of any vector required for position estimation (first null, first active, second active or second null) in the PWM waveform to a reasonable accuracy. To facilitate this, the PM machine was operated as a normal closed loop vector controlled **drive**, obtaining a position measurement from a shaft mounted encoder. The estimated quantities were not used to control the **motor** (hence this testing is referred to as open loop). A 17µs minimum pulse width was applied to the PWM vectors allowing measurements from the Rogowski coil and 2IS method to be taken for **comparison**. The speed and load were set to values used when capturing ANN training data (30Hz, 83% load) to demonstrate the behaviour at what should be a good operating point. Fig. 2 (a)-(d) show the derivative estimates for each of the PWM vectors where one fundamental period is represented by 167 samples on the x-axis.

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Figure 9 shows the SRM Speed of **Fuzzy** **logic** controller. Initially stating Speed of 6900rpm and after 0.05sec finally speed is settled at 4500 rpm. The PI-controller takes decision during steady state to reduce steady state error of the **system** and the **fuzzy** **logic** controller takes decision during transient state to get fast response and low overshoot when the absolute value of speed error is greater than 7 rpm. This set value depends upon the **fuzzy** **logic** controller and the sampling frequency of for the case of steady state, the PI-controller dominates the control output to significantly reduce steady state error of the **system** and the FLC contributes to the output to provide fast response and low overshoot when the absolute value of speed error is higher than 7 rpm.

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