Much work has been done on the analysis of **fuzzy** **control** rules and membership function parameters [4]. The PSO (**particle** **swarm** **optimization**) algorithms were used to get the optimal values and parameters of our FLC. The PSO is based on a metaphor of social interaction. It searches a space by adjusting the trajectories of individual vectors, called ‘particles’, as they are conceptualized as moving as points in multidimensional space. The individual particles are drawn stochastically towards the positions of their own previous best performances and the best previous performance of their neighbours. Since its inception, two notable improvements have been introduced on the initial PSO which attempt to strike a balance between two conditions. The first one introduced by Shi and Eberhart [5] uses an extra ‘inertia weight’ term which is used to scale down the velocity of each **particle** and this term is typically decreased linearly throughout a run. The second version introduced by Clerc and Kennedy [6] involves a ‘constriction factor’ in which the entire right side of the formula is weighted by a coefficient. Their generalized **particle** **swarm** model allows an infinite number of ways in which the balance between exploration and convergence can be controlled. The simplest of these is called PSO. The PSO algorithms are **applied** to choose membership functions and **fuzzy** rules. However, the expert experiences or knowledge are still necessary for the ranges of membership functions. In this paper, a novel strategy is proposed for designing the optimal **fuzzy** controller.

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In **control** engineering, **motor** **control** plays a major role and is an unavoidable part, regardless of whether it is **speed** or position **control**. The effectiveness of a controller can be justified by performance objectives such as settling time, rise time, peak overshoot. **Fuzzy** **control** theory usually provides nonlinear controllers that are capable of performing different complex nonlinear **control** actions [1]. Due to the excellent **speed** **control** characteristics of a **DC** **motor**, it has been widely used in industry (such as cars, locomotives etc.) even though its maintenance costs are higher than the induction **motor**. As a result, authors have paid attention to position **control** of **DC** **motor** and prepared several methods to **control** **speed** of such motors. Proportional–Integral-Derivative (PID) controllers have been widely used for **speed** and position **control** [2]. A **particle** **swarm** **optimization** (PSO) instead of (GA).They presented a PID controller based on (PSO) method of tuning controller parameters. They modeled their PID-PSO controller in MATLAB environment and compare the results with **fuzzy** **logic** controller (FLC) using PSO. They found that PID-PSO controller gives better performance and minimal rise time than FLC-PSO controller [3]- [6], presented a novel optimal PID controller using (LQR) methodology in tuning the parameters of PID controller. The new PID controller is **applied** to **control** the **speed** of brushless **DC** **motor** (BLDC). Finally, the computer simulation and experimental results showed that the proposed controller gives better performance than the traditional controller [7], presented and compared two types of controllers which are PID controller and optimal controller. The PID compensator is designed using (GA), while the other compensator is made optimal and integral state feedback controller with Kalman filter. Computer simulations have been carried out. Finally they found that the second controller gives less settling, less overshoot and better performance encountering with noise and disturbance parameters variations. [8], presented a novel PID dual loop controller for a solar photovoltaic (PV) powered industrial permanent magnet **DC** (PMDC) **motor** drive. MATLAB/SIMULINK was used in the analysis for the GUI environment[9], introduced the optimal strategies for **speed** **control** of permanent magnet synchronous **motor** (PMSM) through the linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) methodologies. In this paper the **fuzzy** **control** schemes are used to perform a comparative study with LQR. The candidate controller is **fuzzy** PID controllers. The detailed derivation of these **fuzzy** controllers is referred to [10]-[11].

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In(2009) Boumediène ALLAOUA and Brahim GASBAOUI and Brahim MEBARKI et al. [7] Discussed the **DC** Intelligent Controller. Use the **Particle** **Optimization** (PSO) method to form the best proportional derivative controller (PID). Adjust parameters. The **DC** controller is designed. environment. Compared with the mysterious logical controller using smart PSO algorithms, the schema. The graph is more efficient in improving the stability of loop response **speed**, the fixed state error is reduced, the time high. With no overrun.

This paper proposes the application of a **particle** **swarm** **optimization** technique for tuning parameters of a PI **speed** controller. The **Particle** **Swarm** **Optimization** (PSO) technique is recently **applied** in a few fields emerging because it's a powerful **optimization** tool. This paper is organized as follows: Section II is give illustrates of mathematical modelling of **DC** **Motor** with a classical PI controller. Section III gives **particle** **swarm** **optimization**. Section IV gives tuning of PI controller based on PSO. The simulation results are discussed in section V.

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The vector **control** or also known as Field Oriented **Control** has been widely used in the induction **motor** **control** has been established as the core servo-drive system for industry application [1]. The vector **control** uses the dynamic mathematical model of induction **motor** and decouples **control** of flux and torque which makes the induction **motor** deliver excellent dynamic performance [2]. In order to get good dynamic performance the parameters of the **speed** controller must be accurate. The methods given in reference [3-5] using ANN and **Fuzzy** **logic** are rendering good performance but they require the additional components in the design, thus increasing the cost of the system.

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scouts. First half of the colony consists of the employed artificial bees and the second half includes the onlookers. For every food source, there is only one employed bee. In other words, the number of employed bees is equal to the number of food sources around the hive. The employed bee whose the food source has been abandoned by the bees becomes a scout. The position of a food source represents a possible solution to the **optimization** problem and the nectar amount of a food source corresponds to the quality (fitness) of the associated solution. The number of the employed bees or the onlooker bees is equal to the number of solutions in the population. In proposed ABC-PID controller, ABC algorithm is used to optimize the gains and the values are **applied** into the controller of the plant. The objective of this algorithm is to optimize the gains of the PID controller for the given plant. The proportional gain makes the controller respond to the error while the integral derivative gain help to eliminate steady state error and prevent overshoot respectively.

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ABSTRACT: This paper deals with the direct torque **control** (DTC) of BLDC **motor** drives by using **particle** **swarm** **optimization** technique. BLDC motors have wide variety of advantages like higher **speed** ranges, higher **efficiency**, and better **speed** versus torque characteristics. Direct torque **control** (DTC) is one of the efficient methods used in variable frequency drives to **control** the BLDC **motor**. DTC offers many advantages like fast torque response, no need of coordinate transformation and less dependence on the rotor parameters. In DTC, the estimated flux magnitude and torque are compared with their reference values. The reference torque is generated from the output of the **speed** regulator (PI controller).Tuning PI parameters (Kp, Ki) are essential to DTC system to improve the performance of the system at low speeds. In conventional PI controller, the performance of the **motor** may cause unexpected torque disturbances. **Particle** **swarm** **optimization** (PSO) is proposed to adjust the parameters (Kp, Ki) of the **speed** controller in order to minimize torque ripple, flux ripple, and stator current distortion.The simulation results of BLDC drive employing conventional PI and PSO based PI controllers is compared and evaluatedunder various load disturbances in the MATLAB/simulink environment.

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dampers, higher **control** levels may be achieved. Furthermore, it is important to reduce the costs, which are related to the set up and maintenance of the semi active systems. On the other side, the minimum vibration magnitude is a crucial criterion for the effectiveness of **control** systems [13]. Several studies perused the optimal placement of dampers but none of them has paid attention to find the optimal MR damper placement and sensors as two discrete subjects. PSO is a novel stochastic evolutionary algorithm, which has been proposed recently [14-17]. It is based on the sociological behavior through the movement and behavior patterns of bird flocks and fish schools. A modified binary **particle** **swarm** **optimization** (BPSO) is used to obtain the optimal placement of MR dampers and sensors with the minimum number of MR dampers. Classical optimal **control** [18] and instantaneous optimal **control** [19] have been **applied** to the structures with known structural parameters. However, they require some previous knowledge or precise information about the characteristics of a structure that its mathematical model is going to be constructed. Moreover, **control** schemes such as Linear Quadratic Gaussian (LQG) optimal **control** necessitate a solution for heavily constrained **optimization** problems [20]. To overcome those obstacles, many studies have focused on soft-computing techniques such as **fuzzy** **logic** [21] and neural networks [22]. Recent studies demonstrated that adaptive controllers are more reliable and effective [23-25]. The focus of this study is semi-active adaptive optimal **control** of 2D benchmark linear buildings under seismic excitation, based on the **fuzzy** **logic** controller (FLC). Effective **fuzzy** **logic** controller used to improve the MR damper **efficiency** and consuming less energy [16]. **Fuzzy** **logic** controller manages the MR damper characteristic by sending electrical input current. In the previous **Fuzzy** **logic** controller, input data are the relative velocity and displacement of MR damper piston. In this study, separate sensors were used independently to transmit the absolute displacement and the velocity of stories. To the Author’s best knowledge, there is no published research on semi active **control** of high-rise building with MR dampers by using separately sensor installation to manage the **control** forces. For this purpose, FLC calculates the magnetic field inducing current regarding to the displacement and the velocity of the floor, which were transmitted by the sensors. The inducing current should be sent to each damper. The proposed PSO-FLC controller demonstrate its **efficiency** with less computational burden by using **particle** **swarm** **optimization** to find the optimal placement and the number of dampers and sensors, simultaneously.

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The PSO technique is an evolutionary computation technique, but it differs from other well-known evolutionary computation algorithms such as the genetic algorithms. Although a population is used for searching the search space, there are no operators inspired by the human DNA procedures **applied** on the population. Instead, in PSO, the population dynamics simulates a ‘bird flock’s’ behavior, where social sharing of information takes place and individuals can profit from the discoveries and previous experience of all the other companions during the search for food. Thus, each companion, called **particle** , in the population, which is called **swarm** , is assumed to ‘fly’ over the search space in order to find promising regions of the landscape. For example, in the minimization case, such regions possess lower function values than other, visited previously. In this context, each **particle** is treated as a point in a d-dimensional space, which adjusts its own ‘flying’ according to its flying experience as well as the flying experience of other particles (companions). In PSO, a **particle** is defined as a moving point in hyperspace. For each **particle**, at the current time step, a record is kept of the position, velocity, and the best position found in the search space so far. The assumption is a basic concept of PSO [12]. In the PSO algorithm, instead of using evolutionary operators such as mutation and crossover, to manipulate algorithms, for a d- variable **optimization** problem, a flock of particles are put into the d-dimensional search space

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This stage introduces different methods that can be used to produce **fuzzy** set value for the output **fuzzy** variable Δw. Here the center of gravity or centroids method will used to calculate the final **fuzzy** value Δw ( ). Defuzzification using COA method means that crisp output of ( ) is obtained by using Centre of gravity, in which the crisp output Δw ( ) variable is taken to be the geometric Centre of the output **fuzzy** variables value (Δw) area, where (Δw) is formed by taking the union of all the contributions of rules with the degree of fulfillment greater than zero. Then the COA expression with discretized universe of discourse can written as:

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This work describes an application of **fuzzy** **logic** system to the **control** of electrical machines. The **fuzzy** **logic** **control** presents a new approach to robust **control**. The **control** methodology is described and used to develop a simple robust controller to deal with uncertain parameters and external disturbances. The design of the FLC depends on the structure adopted in fuzzification, defuzzification and rule base. In this work, a complete **fuzzy** **logic** **control**, based on separately excited **DC** **motor**, has been described. The system was analyzed and designed. The performances were studied extensively by simulation to validate the theoretical concept. To avoid the complexity of the FLC and the decrease of its precision, seven subsets were adopted to describe each input and output variables. It appears from the response properties that it has a high performance in presence of the uncertain plant parameters and load disturbances. The results of this work show that the **control** of **speed** by FLC gives fast dynamic response with minimal overshoot and negligible steady-state error. In future works, the combination of **fuzzy** **logic** and other Artificial Intelligence (AI) techniques such as Genetic Algorithm (GA), **Particle** **Swarm** **Optimization** (PSO) etc. could be performed for **optimization**.

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The area of **DC** **motor** **speed** **control** and analysis in very wide, but Proportional-Integral-Derivative (PID) controllers have gained wide popularity in the **control** of **DC** motors. Their performances, though require some degree of manual tuning by the operator, are still satisfactory but a means of auto-tuning is desirable. In this paper, the performance of a select **dc** **motor** controlled by a proportional-integral-derivative (PID) controller and by a proportional integral (PI) controller is investigated. An overshoot is observed with an accompanied large settling time thereby confirming the behavior of a typical PID controller and PI controller. It is therefore a matter of necessity to tune the PID controller and PI controller in order to obtain the desired performance. On the other hand, a **fuzzy** **logic** based controller **applied** to the **dc** **motor** is investigated. With the application of appropriate expert rules, there is no overshoot and the settling time is within the desired value. With **fuzzy** **logic** controller, manual tuning is eliminated and intelligent tuning takes the Centre stage with satisfactory performance. Therefore in this paper we are comparing the performance of a select **DC** **motor** with the application of PID and PI controller and an auto **fuzzy** **logic** controller and observing the best result.

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use in controller development, especially for many conventional **control** design procedures that require restrictive assumptions for the plant [12], [13].As an alternative, **fuzzy** **control** provides a formal methodology for representing, and implementing a human's heuristic knowledge about how to **control** a system, which may provide a new paradigm for nonlinear systems. **Fuzzy** controller is unique in its ability to utilize both qualitative and quantitative information. Qualitative information is gathered not only from the expert operator strategy, but also from the common knowledge [12], [13]. **Fuzzy** **control** should not be employed if the system to be controlled is linear, regardless of the availability of its model. PID **control** and various other types of linear controllers can effectively solve the **control** problem with significantly less effort, time, and cost.

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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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The use of induction motors has increased tremendously since the day of its invention. They are being used as actuators in various industrial processes, robotics, house appliances (generally single phase) and other similar applications. The reason for its day by day increasing popularity can be primarily attributed to its robust construction, simplicity in design and cost effect iveness. These have also proved to be more reliable than **DC** motors. Apart from these advantages, they have some unfavorable features like their time varying and non-linear dynamics. **Speed** **control** is one of the various application imposed constraints for the choice of a **motor**. Hence, in the last few years it has been studied by many, and various methods for the same have been developed. An insight into the same has been provided in Chapter 2. Out of all the **speed** **control** mechanisms, the Volts/Hertz **control** scheme is very popular because it provides a wide range of **speed** **control** with good running and transient performance. This scheme has been thoroughly explained in Chapter 2. This **control** mechanism is referred to as scalar **control** mode. Here both the input and output commands are **speed**, unlike the Vector **control** mode where it is torque/flux and reference current, respectively. Even though vector **control** drives provide excellent performance in terms of dynamic **speed** regulation, implementation of the same is tedious owing to on -line coordinate transformations that convert line currents into two axis representation and vice versa

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There are mainly two types of losses in induction **motor** copper & iron losses. These losses are stator copper losses And rotor copper losses and another type of losses are stray losses ,iron losses and mechanical (friction+windage) Losses. The main losses are 80%of total losses. The focus of this paper is to minimize the losses of induction **motor** Using **fuzzy** controller. However in many condition these losses are present due to electromagnetic losses. On the other hand many application which require adjustable **speed**/torque. The following thing that permit the electromagnetic loss minimize by the different acts as electromagnetic torque is proportional to the vector product of rotor magnetic flux and rotor current. To obtain the same torque with different combination of flux and current values that is necessary. The purpose of this paper is to introduce a maximum **efficiency** **control** method which does not require knowledge of machine parameters and yield a true optimum at the any load torque and **speed**. in many application **efficiency** **optimization** of induction **motor** which is the most electrical **motor** present an important factor of **control** especially for autonomous electrical traction.

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Guidance is considered the key to the successful performance of any missile. The conventional PNG law, which considered the backbone of guidance laws, becomes more difficult to accomplish the interception task because of the improvement of aircraft maneuverability. Moreover, this operation is hampered by the presence of large number of uncertainties ranging from atmospheric turbulence to variations in radar parameters. The presence of several other factors like sensor noise, inaccurate representation of missile flight **control**, system dynamics, components driven to saturation etc, forms the problem [1]. It is difficult to formulate what the true missile model is and what the behavior of the missile change. It is therefore obvious to design the robust controller which solves the problem.

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For receiving and transmission **motor** **speed** used shaft encoder. Here's another piece are used that called coupling. For the coaxial and transmission **motor** **speed** to shaft encoder because of heterogeneous between two shaft diameter, we need to coupling. This piece was designed so that the input inner diameter 12mm and output inner diameter 6mm. Two screws at the junction of the two shafts are considered to prevent of freewheeling. The basis for the shaft encoder is used. Due to the lower elevation shaft encoder, we needed to a base for strength and to prevent vibrations. Also a board made of MDF for mounting all components were considered, until all pieces are sturdy and without translocation. Fig.13 shows the connection of **motor** shaft and shaft encoder.

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Abstract: Negative output elementary Luo converter performs the conversion from positive **DC** input voltage to negative **DC** output voltage. Since Luo converters are non-linear and time-variant systems, the design of high performance controllers for such converters is a challenging issue. The controller should ensure system stability in any operating condition and good static and dynamic performances in terms of rejection of supply disturbances and load changes. To ensure that the controllers work well in large signal conditions and to enhance their dynamic responses, soft computing techniques such as **Fuzzy** **Logic** controller (FLC) and **Particle** **Swarm** **Optimization** based FLC (PSO-FLC) are suggested. In recent years, **Fuzzy** **logic** has emerged as an important artificial intelligence tool to characterize and **control** a system, whose model is not known or ill defined. **Fuzzy** **logic** is expressed by means of if-then rules with the human language. In the design of a **fuzzy** **logic** controller, the mathematical model is not necessary. However, the rules and the membership functions of a **fuzzy** **logic** controller are based on expert experience or knowledge database. To ensure better performance of **fuzzy** controller, membership functions, **control** rules, normalizing and de-normalizing parameters are optimized using PSO. The main strength of PSO is its fast convergence than the other global **optimization** algorithms. To exhibit the effectiveness of proposed algorithm, the performance of the PSO based **fuzzy** **logic** controller has been compared with FLC and the necessary results are presented to validate the PSO for **control** purposes. Comparative study emphasize that the optimized PSO based **fuzzy** controller provide better performance and superior to the other **control** strategies because of fast transient response, zero steady state error and good disturbance rejection under variations of line and load and hence output voltage regulation is achieved. Simulation studies have been performed using Matlab-Simulink software.

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