Most of the control techniques in industrial applications are embedded with the Proportional-Integral-Derivative (PID) **controller**. PID control is one of the oldest techniques. It uses one of its families of controllers including P, **PD**, PI and PID controllers. There are two reasons why nowadays it is still the majority and important in industrial applications. First, its popularity stems from the fact that the control engineer essentially only has to determine the best setting for proportional, integral and derivative control action needed to achieve a desired closed-loop performance that obtained from the well-known Ziegler-Nichols tuning procedure.

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Brushless **DC** **motor** (BLDC) has been widely used for many years in many industrial application because of its efficiency and reliability. There has been a massive research interest in the area of modelling and control of BLDC as reported in the literatures. Recent research, for instance, an efficient ANFIS based speed **controller** for BLDC **motor** is proposed as in [1]. ANFIS **controller** is modelled through modified data of the **fuzzy** tuned PID **controller** and is simulated under varying speed and load conditions. Control system responses shows that the proposed **controller** is superior to the classical PI and **fuzzy** tuned PID **controller** and **fuzzy** variable structure **controller**. Bat algorithm optimized online ANFIS for the same plant is also proposed [2]. Their results show that the proposed **controller** eliminates the uncertainty problem due to load disturbance and enhance the time domain specifications and performances in all operating conditions. The same algorithm is then enhanced using the same system by proposing a **fuzzy** online gain tuned windup PID and **fuzzy** PID supervised ANFIS [3]. The results show that the windup problem can be eliminated and also improve control system performances.

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The field of electrical energy will be divided into three areas electronics , power and control system. Electronics basically deals with the study of semiconductor device and circuit at low power, power involves generation, transmission and distribution of electrical energy .The electric **motor** which is high performance **motor** drives are very much essential for industrial application. These **motor** require automatic control of their main parameters such as speed, position, acceleration etc. In this paper to control the speed of **dc** **motor** separately excited **dc** drive system is described .So **dc** **motor** is used in many applications such as steel rolling mills, electric vehicles, electric trains, electric cranes and robotic manipulators require speed **controller** to perform its tasks smoothly because of their simplicity, reliability, and low cost **dc** drive have long been used in industrial application. Compared to ac drive **dc** drive are less complex.[6]Many types of controllers such as proportional, integral, derivative, proportional integral(PI), proportional derivative(**PD**), proportiona lintegralderivative(PID),**fuzzy** logic **controller** and optimization techniques have been developed for speed control of **dc** **motor**.

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The importance of PI, **PD** and PID **controller** in process industry cannot be overemphasized because most of the industrial controllers used today are utilized PID , **PD** and PI **controller** as their structure and operation is easily understandable. On the other hand, the use of a **fuzzy** logic **controller** is investigated where the **fuzzy** inference system (FIS) has five membership functions for both input parameters as well as the output parameters for a typical Mamdani-type **controller**. Mamdani-type FIS based **controller** is used because of its closeness to human reasoning and language at both sides of the system, that is, input and output. The research tool for this work is Matlab/Simulink version 8.4, where simulations are run and appropriate behaviors regarding each (PI, PID and **Fuzzy**) **controller** is displayed. Comparison is drawn between the two and three controllers in terms of performance which justifies the direction of modern control engineering practice though there are still rooms for improvement. The other parts of this paper is divided into; The **DC** **motor** model, PID **controller**, tuning of PID, PI **controller** and tuning of PI, **fuzzy** logic **controller**, **fuzzy** logic **controller** algorithm, discussions as well as conclusion. A proportional-integral derivative **controller** (PID **controller**) is a control loop feedback

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Excessive use of fossil fuels in present scenario leads to environmental pollution and mainly affects the global climate and temperature. These problems can be solved by the use renewable energy sources [1]. Solar energy is most available in nature and PV panels are used to convert solar energy into electricity. Power electronics interface is required to drive the **motor** from the panel. Hence, a **DC**-**DC** Push-Pull Converter merge with **DC**-AC three phase inverter, it is possible to transfer power efficiently from panel to the **motor** with the help MPPT Technique. Here, Perturb and Observe MPPT Technique is used to obtain maximum power from panel [2]-[7].

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Abstract:- This paper gives the demonstration about the position control of **DC** **motor** using a **Fuzzy** PID **controller** to meet the desired position in presence of set point changes the most commonly used **controller** in the industry field is the proportional-integral-derivative (PID) **controller**. The PID controllers mostly used in industries due to their robust performance in a wide range of operating conditions & their simple tuning methods. This paper presents design of PID **controller** with Ziegler-Nichols (ZN) technique for controlling the position of the **DC** motors. **Fuzzy** logic **controller** (FLC) provides an alternate to PID **controller**, especially when the available system models are inexact or unavailable. **Fuzzy** logic is one of the most successful applications of **fuzzy** set in which the variables are linguistic rather than numeric. The design of intelligent control systems has become an area of intense research interest. The development of an effective methodology for the design of such control systems undoubtedly requires the synthesis of many concepts from artificial intelligence. The scopes includes the simulation results of the PID **controller**, implementation of **fuzzy** PID **controller** to position of **DC** **motor** In this paper **fuzzy** PID and proportional-integral-derivative (PID) controllers are compared for controlling the position of direct motors(**DC**) motors. Simulation results are demonstrated using MATLAB. Performance analysis results are carried out to analyze the effectiveness of the designed **Fuzzy** PID **controller** as compared to the ZN tuned PID **controller**.

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Zeta converter has been designed. Modeling and control of a Zeta converter operating in Continuous Conduction Mode (CCM) has been presented.The output voltage response as an open-loop system has been analyzed. The Zeta converter was simulated with variation in load. The simulation was done for **DC**-**DC** Zeta converter with the various controllers such as PI **controller**, **Fuzzy** logic **controller** and sliding mode **controller** to regulate the output voltage of the zeta converter circuit. The initiative is taken to develop sliding mode control technique for zeta converter.The computation time for the proposed system gains were very short. Hence the system gives better output and is efficient. The future scope of this work is usage of solar source employing effective MPPT algorithm

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Abstract. This paper is mainly to solve the **fuzzy** control algorithm of the electromagnetic two-wheel balance car when driving on the track. In this paper, by establishing the algorithm and the simulation model of the **motor** parameters used in MATLAB, the simulation results of multiple parameters are compared to obtain the optimal parameters. Then used the programming to combine the adaptive **fuzzy** control algorithm with the directional control loop of the two-wheel balance car, so that the steering performance of the balance car is improved. The simulation result shows that the adaptive **fuzzy** control algorithm has a good effect in solving serious nonlinear problems, which greatly reduces the time when the system reaches stability and reduces the interference. The direction control of the balance car becomes smoother and the running speed is actually improved.

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Speed Control of Dc Motor on Load using Fuzzy Logic Controller (A Case Study of Emergency Lube Oil Pump Motor of H25 Hitachi Turbine Generator). Itaketo 867 – 875[r]

In recent years, permanent magnet **DC** motors are used in variety of application including heater, wiper and air conditioner of car and personal computer. These motors need no excitation current; hence no energy consumption due to field fluxes producing. In addition, PMDC motors have no requirement for winding field therefore size of PMDC motors are smaller than conventional **DC** motors and cost of PMDC motors are relatively lower.

LQR control that designed is classified as optimal control systems. This is an important function of control engineering. It has a very nice robustness property [21]. This attractive property appeals to the practicing engineers. Thus, the linear quadratic regulator theory has received considerable attention since 1950s. The liner quadratic regulator technique seeks to find the optimal **controller** that minimizes a given cost function (performance index). This cost function is parameterized by two matrices, Q and R, that weight the state vector and the system input respectively. These weighting matrices regulate the penalties on the excursion of state variables and control signal. One practical method is to Q and R to be diagonal matrix. The value of the elements in Q and R is related to its contribution to the cost function. To find the control law, Algebraic Riccati Equation (ARE) is first solved, and an optimal feedback gain matrix, which will lead to optimal results evaluating from the defined cost function is obtained. Linear quadratic regulator design technique is well known in modern optimal control theory and has been widely used in many applications. The standard theory of the optimal control is presented in Dissertation. Under the assumption that all state variables are available for feedback, the LQR **controller** design method starts with a defined set of states which are to be controlled [22]. The theory of optimal control is concerned with operating a dynamic system at minimum cost. The case where the system dynamics are described by a set of linear differential equations and the cost is described by a quadratic function is called the LQ problem. One of the main results in the theory is that the solution is provided by the linear-quadratic regulator (LQR). The performance measure is a quadratic

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In paper we derived the **fuzzy** based model along with **fuzzy** **controller** of an inverted pendulum controlled by a **DC** **motor** using T-S **fuzzy** model by using the concept given by sector nonlinearity which convert original uncertain nonlinear system into linear sub-system with uncertainty. Then the linear sub-system is analysed and its performance is checked with system without uncertainty. The paper is organised as: In section 2 we comes with the problem formulation of an inverted pendulum controlled by a **DC** **motor** by using T-S **fuzzy** model with uncertainty and design of **fuzzy** **controller** by pole placement method, In section 3 the stability of an approximate linear sub- system using theorem derived by H. K. Lam, F. H. F. Leung, and P. K. S. Tam is given. In section 4 we show the results of uncertain **fuzzy** control system and finally the conclusion is drawn.

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Suatu sistem kendali yang baik harus mempunyai ketahanan terhadap disturbance dan mempunyai respon yang cepat dan akurat. Sering terjadi permasalahan dalam sistem kendali Proportional Integral Derivative (PID) bila dibuat sangat sensitif, maka respon sistem terhadap disturbance menghasilkan overshot/undershot yang besar sehingga kemungkinan dapat terjadi osilasi semakin tinggi. Bila dibuat kurang sensitif memang akan menghasilkan overshot/undershot kecil, tetapi akibatnya akan memperpanjang recovery time. Untuk mengatasi hal ini, diterapkan sistem kendali hybrid yaitu sistem kendali PID yang akan dihybridkan dengan sistem kendali logika **fuzzy**. Dalam sistem ini kendali utama adalah kendali PID sedangkan kendali logika **fuzzy** bekerja membantu untuk meminimalkan overshot/undershot yang terjadi dan juga meminimalkan recovery time dari respon sistem. Sistem kendali logika **fuzzy** yang didesain mempunyai 2 input yaitu error dan delta error dan output kecepatan **motor**. Besar output dari sistem kendali logika **fuzzy** hanya 50 % dari kendali PID. Hal ini dilakukan dengan membatasi semesta pembicaraan dari himpunan **fuzzy** untuk output. Dari desain sistem ini diharapkan sistem kendali secara keseluruhan yang merupakan hybrid antara PID dengan Kendali Logika **Fuzzy** dapat menghasilkan respon sistem yang lebih baik.

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In recent days, the Permanent magnet brushless **DC** **motor** (PMBLDCM) drives [1-2] are extensively used for many applications ranging from servo control to traction control. The invention of high energy rare earth permanent magnets have widely enhanced the applications of Brushless **DC** (BLDC) motors in order to meet the competitive world wide market demands of manufactured goods, devices, products and processors. The BLDC drives are suitable in clean, explosive environments such as robotics, machine tools, aeronautics, robotics, electric vehicles food and chemical industries [3-5] because of high efficiency, silent operation, compact form, reliability and low maintenance.

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Brushless **DC** (BLDC) motors are one of the most interesting motors, not only because of their efficiency, and torque characteristics, but also because they have the advantages of being a direct current (**DC**) supplied, but eliminating the disadvantages of using Brushes. BLDC motors have a very wide range of speed, so speed control is a very important issue for it. There are a lot of parameters which need to be in focus while talking about a speed **controller** performance like starting current, starting torque, rise time, etc. There are two main methods for controlling the speed, PID Controllers, and **Fuzzy** PI controllers. Both are different in complexity and performance. In this paper, the PI and **Fuzzy** PI speed controllers for the BLDC motors will be proposed. A simulation study is conducted to evaluate the efficiency of the proposed speed controllers. Further, a comparative study is performed to validate the system effectiveness.

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In this section, the review of previous and related research about bionic hand was conducted. The research focuses more about design criteria for challenging task such as system identification and design **fuzzy** logic rule. The **fuzzy** logic need the two variables input to controlling the error and derivative of error. The output **fuzzy** **controller** will determine and assign a suitable the defuzzification method before apply into plant system. The aspect on this review will consider as a research to develop a better prosthetic hand followed the requirement and material. A material that being used must be durable, light and easy to operate and low cost.

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Abstract— Vehicle with advanced control technology in its power-train system offer many advantages in terms of performance, fuel consumption and improvement of driving comfort. This paper aims to provide a contribution in this direction by proposing a **PD** **controller** for an electronically controlled dry friction clutch engagement system. The engagement process for a novel Electro-Mechanical Friction Clutch (EMFC) is being studied and evaluated semi-empirically which exploits static and dynamic models for the system. This EMFC will be integrated with a novel Electro-Mechanical Dual Acting Pulley (EMDAP) continuously variable transmission (CVT) for the next generation of an automotive transmission in the future. Simplified static and dynamic models of the EMFC system consisting of mechanical actuator, dry friction clutch, a **DC** **motor** system and clutch linkages were developed. The **DC** **motor** system is used as an actuator to actuate power screw mechanism for engaging and disengaging the clutch. Matlab/Simulink® is used to simulate the model engagement process based on the experimental values of open-loop clutch engagement process. Computer simulation results are presented to demonstrate the effectiveness of the proposed control scheme for the closed loop system.

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ABSTRACT: This paper presents about the Power Factor Correction based Bridgeless LUO converter fed Switched Reluctance **Motor** drive by using **Fuzzy** **Controller**. The proposed topology aims at designing and modelling of a Bridgeless LUO converter for driving a Switched Reluctance **Motor** in a simple and effective manner. The proposed control scheme has the concept of **DC** link voltage control relative to the desired speed of the Switched Reluctance **Motor**. Bridgeless LUO converters are the combination of two **dc** to **dc** converters with less semiconductor switches. The C-Dump converter is used for the Switched Reluctance **Motor** which results in power factor correction and phase de-fluxing component with reduced device count. The current regulation is achieved by this converter and it is suitable for low voltage applications. The result of **fuzzy** logic **controller** to a SRM drive gives the better performance and high robustness.

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ABSTRACT:This paper uses NEURO **FUZZY** TECHNIQUE in estimating speed and controlling it for a separately excited **DC** **motor**. The rotor speed of the **dc** **motor** can be made to follow an arbitrarily selected trajectory. The purpose is to achieve accurate trajectory control of the speed of separately excited **DC** **Motor**, especially when the **motor** and load parameters are unknown.