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The **controller** can specifically control a process to the requirement by tuning the 3 components in the PID **algorithm**. The controller’s response can be in the form of the degree of setpoint overshooting by the **controller**, the responsiveness of the **controller** to an error, and the rate of system oscillation. It is worthy to note that using the PID **controller** does not ensure an optimal system stability. Some systems may need the use of 1 or 2 modes for an efficient control. This is achievable through setting the gain of undesired outputs to zero. Without the respective control actions, a PID **controller** can be referred to as either a PI, **PD**, P or I **controller**. The PI controllers are common due to the sensitivity of the derivative action to measurement noise; while the system may be prevented from attaining the target by the absence of an integral value as a result of control action. Further details on the PID control system is provided by [29].

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This paper presents an anti-windup self-tuning euzzy proportional-integral-derivative (PID) **controller** for the **speed** control of **brushless** **direct**-**current** (BLDC) **motor**. The proposed **controller** consists of a self-tuning **fuzzy** PID **controller** and an snti-windup **controller**, which combines the merits of these two controllers. The anti-windup **controller** is designed as a **fuzzy** proportion **controller** and is switched by a logic function **based** on the output of proposed **controller** and the **speed** error. A detailed simulation study in MATLAB/Simulink is performed to investigate the performance of proposed **controller**, and the simulation experiments are conducted on different conditions. The control system parameters of the proposed **controller** such as overshoot, settling time and rising time are compared with that of the conventional PID **controller**. The simulation results show that the proposed **controller** can accelerate the response **speed** of BLDC **motor**, strengthen the ability of anti-windup and make **motor** runs smoothly, it is proven that anti-windup self-tuning **fuzzy** PID **controller** has better performance than conventional PID **controller**.

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Increasing the methods of order calculus for Fractional Order Proportional Integral Derivative (FOPID) **controller** leads to a wide applications for this type of **controller** in control systems. A closed loop **speed** control for **BrushLess** **Direct** **Current** (BLDC) **motor** with FOPID **controller** runs the **motor** very close to the reference **speed**, provides a good performance and robustness compared with a corresponding system using conventional PID **controller**. In this paper, the BLDC **motor** is modeled and simulated in Matlab/Simulink for **speed** control strategy. A DC link **speed** control strategy is proposed and implemented. The FOPID **speed** **controller** parameters (Kp, ki, kd, λ ) are **optimized** by genetic evolutionary programming module. The effectiveness of the proposed **speed** control is verified through simulation. The control system is extra tested under a conditions of some **motor** parameters perturbations. The simulation results reveal a flexible and stable control strategy.

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Abstract: **Direct** Torque Control of the sensor less permanent magnet **brushless** DC **motor** with **fuzzy** logic **speed** **controller** is presented in this paper. The **direct** torque control is one of the vector **based** control in variable frequency drive control. This project proposes the control of torque directly and stator flux amplitude indirectly by using **direct** axis **current**. The torque error, flux error and estimated position of the stator flux linkage vector are used to change the switching sequence of the inverter through space vector pulse width modulation to control the **motor** **speed**. An impedance source network is coupled between the inverter and the DC supply to reduce the ripples in torque by boosting the inverter’s input voltage. The three phase voltage and **current** are converted to d- q axis to calculate the stator flux linkage vector, rotor angle and actual torque using park transformation. A **fuzzy** logic **controller** is used to generate a reference torque value by processing the **speed** errors. A prototype model is made for 24 V BLDC **motor** and the closed loop operation is obtained using **fuzzy** logic **controller**.

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ABSTRACT: **Brushless** **Direct** **Current** (BLDC) motors are one of the **motor** types rapidly gaining popularity. BLDC motors are widely used in industries such as Appliances, Automotive, Aerospace, Consumer, Medical, Industrial Automation Equipment and Instrumentation. BLDC motors are receiving wide attention due to their high efficiency, high dynamic response, better **speed** versus torque characteristics and small size. Conventional controllers are widely used to control BLDC motors. But they often fail to control the BLDC as they suffer from uncertain parameters and the non-linearity of the BLDC motors. **Fuzzy** control can be used to control the **speed** of the BLDC motors. In this paper, a **fuzzy** logic **controller** whose parameters are tuned on line is proposed to control the **speed** of a BLDC **motor** and is compared with conventional controllers like PI and PID.

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Position sensors can be totally wiped out, along these lines diminishing further cost and size of **motor** gathering, in those applications in which just variable **speed** control (i.e., no positioning) is obliged and framework flow is not especially requesting (i.e., gradually or, at any rate, typically differing burden). Truth be told, some control techniques, for example, back-EMF and **current** detecting, give, as a rule, enough data to assess with adequate exactness the rotor position and, thusly, to work the **motor** with synchronous stage streams. A PM **brushless** drive that does not oblige position sensors but rather just electrical estimations is known as a sensorless drive [4]. The BLDC **motor** gives an alluring contender to sensorless operation on the grounds that the way of its excitation intrinsically offers a minimal effort approach to concentrate rotor position data from **motor**-terminal voltages. In the excitation of a three-stage BLDC **motor**, with the exception of the stage compensation periods, just two of the three stage windings are leading at once and the no directing stage conveys the back-EMF. There are numerous classifications of sensorless control methods [6]; nonetheless, the most famous class is taking into account back electromotive powers or back-EMFs [7]. Detecting back-EMF of unused stage is the most cost effective technique to get the substitution succession in star injury motors. Since back-EMF is zero at stop and corresponding to **speed**, the deliberate terminal voltage that has extensive sign to- commotion proportion can't identify zero intersection at low speeds. That is the motivation behind why in all back- EMF-**based** sensorless strategies the low-**speed** execution is restricted, and an open-circle beginning system is obliged [8].

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The average value of voltage (and **current**) fed to the load is controlled by turning the switch between supply and load on and off at a fast pace. The longer the switch is on compared to the off periods, the higher the power supplied to the load is. The PWM switching frequency has to be much faster than what would affect the load, which is to say the device that uses the power. Typically switching have to be done several times a minute in an electric stove, 120 Hz in a lamp dimmer, from few kilohertz (kHz) to tens of kHz for a **motor** drive and well into the tens or hundreds of kHz in audio amplifiers and computer power supplies.

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The future work of this is to control the **speed** of BLDC **motor** using intelligent **fuzzy** logic **controller**. Thus the control using FLC will be more effective than this PI **controller**. The Future work is to implement this simulation in hardware. They have the advantage to be robust and relatively simple to design as they do not require the knowledge of the exact model. It has simple features like fixed and uniform input and output scaling factors, flat, single partition rule-base with fixed and non-interactive rules, fixed membership functions, limited number of rules, which increase exponentially with number with the number of inputs, fixed knowledge, low-Level control and no hierarchical rule structure.

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In DTC, the optimum voltage space vector for the entire switching period controls the torque and flux independently and the hysteresis band maintains the errors. Only one vector is applied for the entire sampling period, in the conventional method. So, for small errors, the upper or lower torque limit may be exceeded by the **motor** torque. Instead, the torque ripple can be reduced by using more than one vector within the sampling period. The insertion of zero vector precisely controls the slip frequency [8]. For a smaller hysteresis band, the frequency of operation of the PWM inverter could be very high. The width of the hysteresis band causes variation in the switching frequency. **Direct** torque control **based** on space vector modulation preserve DTC transient merits, furthermore, produce better quality steady state performance in a wide **speed** range. At each cycle period, SVM technique is used to obtain the reference voltage space vector to exactly compensate the flux and torque errors. The torque ripple of DTC-SVM in low **speed** can be significantly improved.

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The **speed** control of the BLDC **motor** is studied and simulated in MATLAB/Simulink.The **speed** control of a BLDC **Motor** is presented in this paper, using both PI **controller**, and **Fuzzy** Logic Controller.The inference which can be concluded after comparison is that **speed** control of BLDC using **Fuzzy** Logic **Controller** has better performance than PI,PID Controllers. To add **current** control function to the proposed **speed** **controller** in order to keep the **current** within a certain range for a specific **speed**, could be a work for future. The proposed future work would thereby enhance the **motor** start-up **current**, reduce the **motor** **current** ripples and overall enhance the **motor** torque characteristics performance. **Current** control methodology will also reduce the **speed** and torque variations caused due to any sudden changes in the **motor** **current** value .

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This paper presents the simulation of the control of doubly star induction **motor** using **Direct** Torque Control (DTC) **based** on Proportional and Integral **controller** (PI) and **Fuzzy** Logic **Controller** (FLC). In addition, the work describes a model of doubly star induction **motor** in α-β reference frame theory and its computer simulation in MATLAB/SIMULINK®.The structure of the DTC has several advantages such as the short sampling time required by the TC schemes makes them suited to a very fast flux and torque controlled drives as well as the simplicity of the control algorithm.the general- purpose induction drives in very wide range using DTC because it is the excellent solution. The performances of the DTC with a PI **controller** and FLC are tested under differents speeds command values and load torque.

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The **speed** control is one of important component in **Direct** **Current** **motor** (DC **motor**) operation. In controlling the **speed** of a DC **motor**, some improvement needs to be done toward **speed** regulation during transient loading conditions. The control includes a regulating circuit that having an output for controlling the armature voltage to the **motor**. An input **speed** reference signal corresponding to the desired **speed** is provided to the regulating circuit. A feedback signal proportional to armature voltage is also provided to the regulating circuit to establish an error signal for operating the control to regulate the voltage to the **motor** and therefore the **motor** **speed**.

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Over the past decades, many schemes are introduced for the rotor **speed** estimation in the sensor less vector control of IMDs [4-10]. Some of the methods are (i) method of signal injection [4], (ii) method of state observers [5], (iii) method **based** on model configurations [6]. Signal injection method tolerates from difficulty calculations and essential for the outer equipments for the signal injection method. According to all the schemes of MRAS [7-10] **speed** estimators is usual working procedure because of their good characteristics, merely contrasting the scheme of injecting signal. Back-emf [4], rotor-flux [7], reactive power [5] and active power [10] are the most desired methods of MRAS. The method of back emf which gives good execution when speeds are high, although **speed** estimation is more complex at initial condition and it possess less speedy and hardly empathetic to **motor** constant. Hence, to prevent these disadvantages, rotor flux of MRAS method is most popular and it was first proposed by C Schnauzer in [11].

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Abstract: Ease control and high power density are the two main reasons for using **brushless** DC **motor** in wide range of industrial applications. In general the **brushless** DC motors are controlled using power Semiconductor bridge. So the rotor position sensors are used in the inverter bridge for starting and to provide proper commutation sequence to the power switches. In building inverter bridge power switches are used due to high reliability and performance. In this paper the three phase **brushless** DC **motor** model is designed with proportional integral **controller** and tested in MATLAB software. The PI **controller** is used to control the **speed** of the **brushless** DC **motor**. On the other hand parameters like Back EMF, **current**, **speed** and torque are evaluated for the designed models of BLDC **motor**. Due to the easy implementation and simple control structure the convectional PI **controller** are used in industries.

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For the tuning of the parameters of the membership functions of a **fuzzy** **controller** a novel PSO **algorithm** has been developed. The **algorithm** for the **fuzzy** **controller** has been encoded in MATLAB but a block diagram strategy is enabled to explain the **algorithm**. A SIMULINK model has been used.The Plant used is an armature controlled DC **Motor**. Conventional controllers like PI and PID controllers fail in case of non linearities and may generate steady state error[1]. In such a case a **fuzzy** **controller** is used which is basically a non-linear element whose parameters are tuned using Particle Swarm Optimization Technique (PSO) subject to the condition that steady state error is to be minimized. The quantity to be controlled is the **speed** of the DC **Motor**. Therefore error in **speed** is to be minimized. PSO technique is a very uncertain **algorithm** that may or may not converge to the **optimized** values. Nevertheless we got optimistic simulation results. As such it could overcome the limitations of conventional controllers[1].

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According to Table 2, the four sub-operating modes have different rules to phase **current**. In mode 11, and − rise quickly, and varies proportionally with the back-EMF of phase c. In mode 12, and − drop quickly, and changes proportionally with the back-EMF of phase c. Compared with modes 11 and 12, falls much quicker in mode 13 and rises much quicker in mode 14. Thus, mode 1 could be **optimized** by reasonable combination of four sub-operating modes. First of all, the equation = 0 must be satisfied to keep the system stable as follows. When deviates seriously from zero, modes 13 and 14 works. When remains at zero, modes 11 and 12 works. Because and cannot be detected, a **speed** loop is used here to decide the duty of PWM signals. The same regulating method is used in mode 4, and asymmetric voltage vectors are eliminated.

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Conventional proportional integral (PI) dynamic controls for **speed** and the load have drawbacks [1]. Although **fuzzy** logic is commonly applied to the field oriented control of induction motors, the fuzzification, defuzzification, and decision procedures that establish a knowledge base are more complicated, difficult and time-consuming. Moreover, although some studies [2]-[6] have been performed in this area, most, if not all, are **based** on conventional trial-and-error techniques. However, optimal performance may not be achieved. The phase plane technique [7]-[9] is now widely used to enhance the control performance in the transient and steady states. This paper describes a new method for advancing the indirect oriented field control (IFOC) [3], [4] technique for controlling the **speed** load of an induction **motor**. The proposed **controller** (FPPC) speeds up the response because the defuzzification procedure and the **fuzzy** rules derived from expert experiences (knowledge bases) are no longer required. The block diagram of the proposed **controller** design has shown in Fig. 1 for controlling **speed** load of an induction **motor**.

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Abstract— The induction **motor** is without doubt the most used electrical **motor** because of its unique characteristics. Most of its applications need fast and intelligent **speed** control system.This paper presents an intelligent and advanced **speed** control **based** on **fuzzy** logic technique to achieve maximum torque and efficiency.A rule **based** Mamdani type **fuzzy** logic **controller** is applied to closed loop induction **motor** model.Scalar control(volt-hertz) method is used for controlling speed.A conventional **controller** is compared practically to **fuzzy** logic **controller** using Matlab/Simulink software package.The simulation results show the superiority of the **fuzzy** logic **controller**.

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