Abstract—This paper presents the **position** **control** of **DC** **motor** by using a **fuzzy** **logic** **controller** (FLC) with MATLAB application. Firstly, the **DC** **motor** was modeled and converted to a subsystem in Simulink. Then the **fuzzy** **logic** **controller** is designed by using MATLAB toolbox. The scopes include the simulation and modeling of **DC** **motor**, **fuzzy** **controller** and different transfer function for **DC** **motor** model as a benchmark to the performance of the **fuzzy** system. The **position** **control** is an adaptation of assisted lifting device system. **Fuzzy** **logic** **control** can play an important role because knowledge-based design rules can be easily implemented in the system with unknown structure and it is going to be popular since the **control** design strategy is simple and practical. This makes FLC an alternative method used in a nonlinear industrial system. The results obtained from FLC are compared with different transfer function used for the dynamic response of the closed-loop system and also the system with and without a **controller** will be compared. Parameters such as peak **position** in degree, settling time in second and maximum overshoot in percent will be part of the simulation result. The overall performance shows that system with FLC performs better than compared to the system without FLC.

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Diode Dfp is connected in parallel to the primary winding of the transformer and diode Dr is serially connected with secondary winding across the **dc** link. There is one snubber Capacitor connected in parallel to each switch of phase leg. The snubber capacitor resonates with the primary winding of the transformer. The emitters of the three auxiliary Switches are connected together. Thus, the gate drive of these auxiliary switches can use one common output **dc** Power supply in an entire PWM cycle, all the six main switches can be turned off in the ZVS condition as the snubber capacitors (Cra, Crb, Crc) can slow down the voltage rise rate. This enables the turn-off power losses be reduced and the turn-off voltage spike eliminated. Before turning on the main switches, the corresponding auxiliary switch (Sa, Sb, Sc) must be turned on ahead. Main switches get the ZVS condition

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The “Real-Time **Fuzzy** **Logic** **Position** Controlled **DC** **Motor** Drives” project is needed interface software and hardware. This project is build to **control** the performance of **dc** **motor** for positioning purpose and controlled by **fuzzy** **logic** **controller**. Beside that, this project is build to comprehend the basic concepts of **fuzzy** **logic**. In this chapter will discuss about general background, concept of project, objective, scope, problem statement and report outline.

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PID still very preferred in industry and at many automatic **control** applications, and it also used with BLDC **motor**, but the problem which rises with PID technique are the non-linear systems, the problem of affecting the speed after adding any additional loads, suffering from changing dynamics after a long time operation which will be very difficult to be covered with a fixed PID **controller**. Moreover, the external noise which make PID **controller** not be the perfect choice to **control** the BLDC **motor** in these circumstances because it means that we need to change the parameters dynamically. All of these reasons leading us to think about a more flexible and suitable **controller** like **fuzzy** **logic** **controller** which depends on the linguistic rules that make the system more flexible and more dynamic. And if we need to talk about a more dynamic **controller** shifting us from a flexible **controller** to a new zone of an intelligent **controller** we will talk about using ANFIS (Adaptive Neuro **Fuzzy** Inference Systems) **controller** which makes our system dynamic because of using training , learning and testing techniques which make an enhancement in the performance [3].

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ABSTRACT: This paper presents some design approaches to hybrid **control** systems combining conventional **control** techniques with **fuzzy** **logic**. Such a mixed implementation leads to a more effective **control** design with enhanced system performance and robustness. While conventional **control** allows diverse design objectives such as steady state and transient characteristics of the closed loop system to be precise, **fuzzy** **logic** are to overcome the problems with uncertainties in the plant constraints and structure encountered in the classical model-based propose. Induction motors are characterised by multifarious, highly non-linear and time-varying dynamics and isolation of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector **control** techniques has partially solved induction **motor** **control** problems; because they are sensitive to drive parameter variations and performance can deteriorate if conventional controllers are used. **Fuzzy** **logic** controllers are considered as potential applicant for such an application. Two **control** approaches are developed and compared to adjust the speed of the drive system. The first **control** design is only the vector **control**. And second one is a **fuzzy** state feedback **controller** is developed. A simulation study of these methods is presented and results are compared. The effectiveness of this **controller** is demonstrated for different operating conditions of the drive system.

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Most of the **fuzzy** **logic** applications with the physical systems require a real-time operation to interface high speed constraints. The simple and usual way to implement these systems is to realize it as a software program on general purpose computers, these ways cannot be considered as a suitable design solution. Higher density programmable **logic** device such as Field Programmable Gate Arrays (FPGAs) can be used to integrate large amounts of **logic** in a single IC. FPGAs are one of the fastest growing parts of the digital integrated circuit market in recent times. Dynamically reconfigurable FPGA systems can adapt to various computational tasks through hardware reuse. FPGA becomes one of the most successful of technologies for developing the systems which require a real time operation. FPGAs are more sufficient than the simple way because they can cover a much wider range of operating conditions. Semi-custom and full-custom Application Specific Integrated Circuit (ASIC) devices are also used for this purpose but FPGA provide additional flexibility: they can be used with tighter time-to-market schedules. FPGA places fixed **logic** cells on the wafer, and the FPGA designer constructs more complex functions from these cells [9].In **control** applications, in order to get better **control** responses, [10], [11] **Controller** is implemented in FPGA.FPGA are two dimensional arrays of **logic** blocks and flip-flops with an electrically programmable interconnection between **logic** blocks. The interconnections consist of electrically programmable switches which is why FPGA differs from Custom ICs, as Custom IC is programmed using integrated circuit fabrication technology to form metal interconnections between **logic** blocks. FPGA provides its user a way to configure: The intersection between the **logic** blocks and the function of each **logic** block. **Logic** block of an FPGA can be configured in such a way that it can provide functionality as simple as that of transistor or as complex as that of a microprocessor. It can used to implement different combinations of combinational and sequential **logic** functions.

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The simulated results show that, using a type-2 FLC in real world applications can be a good option since this type of system is a more suitable system to manage high levels of uncertainty, as we can see in the results shown in Tables 2-5. Simulation results indicate that the per- formance of the GA FLCT2 will better. That is mean the system will sense for change the value of IAE and ITAE. The results demonstrate that a type-2 FLC can outper- form type-1 FLCs that have more robustness design pa- rameters. The main advantage of the type-2 FLC appears to be its ability to eliminate persistent oscillations, espe- cially when unmodelled dynamics were introduced. This ability to handle modeling error is particularly useful when FLCs are tuned offline using GA and a model as the impact of unmodelled dynamics is reduced. The sig- nificance of the work is focused to manage the uncer- tainty of the system. The nonlinear of the systems are big problem therefore the one of successful methods to eli- minate or reduce nonlinearity system by using **fuzzy** type two. It is a good option for real time applications that limited time is needed such as robot system.

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The pioneering work on **fuzzy** **logic** was done by Professor Lofti A. Zadeh of the University of California at Berkeley in 1965. He proposed the **fuzzy** set theory which led to a new **control** method called ‘**fuzzy** **control**’. The work of Zadeh began to gain recognition after Dr. E.H. Mamdani of London University practically applied the concept of **fuzzy** **logic** to the automatic **control** of steam engine in 1974. During the past few decades, **Fuzzy** **Logic** has been widely applied in almost all aspects of our society including industrial manufacturing, automatic **control**, automobile production, banks, hospital, etc. Today, **fuzzy** **logic** is still well recognized. Worldwide, thousands of studies are carried out yearly on this field [2], [3].

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This paper contains, performance of **fuzzy** controllers which evaluated and compared. It also describes the speed **control** based on Linear Quadratic Regulator (LQR) technique. The comparison is based on their ability of controlling the speed of **DC** **motor**, which merely focuses on performance of the controllers, and also time domain specifications such as rise time, settling time and peak overshoot. The **controller** is designed using MATLAB software, the results shows that the **fuzzy** controllers are the good but it as higher overshoot in comparison with optimal LQR. Thus, the comparative study recommends LQR **controller** gives better performance than the other controllers.

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In this paper, performance of the FLC and PID **controller** is observed for separately excited **DC** **motor** speed **control**, when input function is step. A system should be designed for less rising time and less overshoot percentage. The performance comparison study between FLC and PID **controller** whose parameters adjusted using Simulink Tuning Tool which gives better parameters than other tuning methods is carried out. Even though, better tuned PID **controller** may be provide better performance, the recommended FLC has more benefits, such as better static and dynamic performance, higher flexibility, **control**, compared with PID **controller**. Furthermore, it can be seen in Figure 10-11, FLC have provided less overshoot percentage of 4.2 % and lower rising time 2.8386 second, which means FLC has better performance in terms of overshoot percentage, settling time, **control** performance, and rise time, therefore, FLC design was recommended and implemented. REFERENCES

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Recently, BLDC motors are very popular compared with the brushed **DC** motors. In short, BLDC motors have some advantages over conventional brushed **DC** motors and induction **motor**. Some of these advantages are better speed versus torque characteristics, high dynamic response, high efficiency, high power density, long operating life, noiseless operations and has higher speed ranges. In addition, BLDC motors are reliable, easy to **control**, low maintenance and it is inexpensive [2], [3], [4]. Due to their favourable electrical and mechanical properties, thus they are widely used in servo applications such as automotive, aerospace, medical instrumentation, robotics, machine tools, textile, industry automation equipment and many more as studied by researchers like Lee and Ehsani (2003). The small sized of motors with external rotors are widely used in visual equipment such as computer disc drives, tape recorders and digital audio tapes [5]. BLDC motors are one of the **motor** types rapidly gaining popularity. Therefore, the **motor** must be controlled precisely so that it will give the desired performance in all these applications.

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A **fuzzy** **control** system is a **control** system in which a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital **logic**, which operates on discrete values of either 1 or 0 (true or false, respectively). **Fuzzy** **Logic** provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. **Fuzzy** Logic’s approach to **control** problems mimics how a person makes decisions, only much faster. Fuzziness is connected with the degree to which events occur rather than likelihood of their occurrence.

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The main motive of constructing a PID **controller** is to regulate and **control** the speed performance of **DC** **motor**. In PID **controller**, the proportional action is used for the amplification with an adjustable gain , the integral **control** action is called as reset **control** and the derivative **control** action is called as rate **control**. When all the three **control** actions are merged then this **controller** is known as PID **controller**. The simulink diagram of speed response with PID **controller** is given below :-

Now imagine a light bulb with a switch. When the switch is closed, the bulb goes on and is at full brightness, says 100 Watts and when the switch is closed is 0 Watts. Now if the switch is closed in a fraction of a second and then opens for the same amount of time, the filament would not have time to cool down and heat up. This will just get an average glow of 50 Watts. This is how lamp dimmers work, and the same principle is used by speed controllers to drive a **motor**. When the switch is closed, the **motor** sees 12 Volts, and when the switch is open it sees 0 Volts. If the switch is open for the same amount of time as it is closed, the **motor** will see an average of 6 Volts, and will run more slowly accordingly. As the amount of time that the voltage is on increases compared with the amount of time that it is off, the average speed of the **motor** increases.

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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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The project is titled as “Development of **Fuzzy** **Logic** Speed **Controller** for **DC** **Motor** Applications by Using Rabbit Microprocessor.” In this project, the embedded **controller** developed is based on Rabbit microprocessor and its core module model RCM 3100. The **control** method implemented in this is **fuzzy** **logic** **control** method. A **fuzzy** **logic** algorithm needs to be developed and be compiled in Rabbit microprocessor in order to provide precise **control** signal for **DC** **motor** variable speed drive. Instead of using assembly language to compile the **fuzzy** algorithm, this **controller** will use Dynamic C programming language to develop the algorithm.

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Figure 2.1 shows a **fuzzy** **logic** **control** system in a closed loop **control**. there are four important elements in the **fuzzy** **logic** **controller** system structure which are fuzzifier, rule base, inference engine and defuzzifier. Details of the **fuzzy** **logic** **controller** system structure can be seen in in figure below. Firstly, a crisp set of input data are gathered and converted to a **fuzzy** set using **fuzzy** linguistic variables, **fuzzy** linguistic terms and membership functions. This step also known as fuzzification. Afterwards, an inference is made base on a set of rules. Lastly, the resulting **fuzzy** output is mapped to a crisp output using the membership functions, in the defuzzification step.

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This paper presents Hybrid **fuzzy** **logic** **controller** (HFLC) for the well developed and sophisticated simulation model of Brush less **DC** (BLDC) **motor** drive using MATLAB. The developed simulation model has been examined by the PID **controller** and HFLC. The performance of the controllers is evaluated more precisely from various simulation studies for variations in the load torque and speed of BLDC **motor** drive. A performance comparison of two controllers is also carried out by taking various performance measures such as settling time, steady state error, peak overshoot, the integral of the absolute value of the error (IAE) and the integral of the time-weighted squared error (ITSE). The results confirm that the developed simulation model is very convenient for the precise evaluation of performance and the HFLC shows improved performance over the PID **controller** in terms of disturbance rejection or parameter variation.

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Abstract— This paper presents a comparative analysis of speed **control** of brushless **DC** **motor** (BLDC) drive fed with conventional two-level, three and five level diode clamped resonant inverter (**DC**- MLI). The performance of the drive system is successfully evaluated using **Fuzzy** **Logic** (FL) based speed **controller**. The **control** structure of the proposed drive system is described. The speed and torque characteristic of conventional two-level inverter is compared with the three and five-level resonant inverter (MLI) for various operating conditions. The three and five level diode clamped resonant inverters are simulated using IGBT’s and the mathematical model of BLDC **motor** has been developed in MATLAB/SIMULINK environment. The simulation results show that the **Fuzzy** based speed **controller** eliminate torque ripples and provides fast speed response. The developed **Fuzzy** **Logic** model has the ability to learn instantaneously and adapt its own **controller** parameters based on disturbances with minimum steady state error, overshoot and rise time of the output voltage.

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Converter operation in steady-state is best described over a period that begins from the phase α to 2п+ α. This operation involves two circuit modes during a single period of the source waveform depending upon the state of the switches as shown in Figure 2.8. Mode 1 starts when the SCRs T1 and T3 are turned on at an angle α by **control** pulses applied at their gate terminals. During mode 1, SCRs T1 and T3 are in forward-biased mode and SCRs T2 and T4 are in reverse blocking mode. The current Io flows through the path shown in Figure 2.11. After angle п, the input source voltage become negative but the SCRs T1 and T3 still conducting. Note that the current sink is the model of a high value inductor, voltage across it can change instantaneously but current cannot. Hence, the output voltage, Vo become negative and follows the input voltage, Vs waveform. The input source is supplying power to the load during α to п which is referred also as the rectifier operation.

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