Top PDF Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

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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Comparison of Performance Measures of Speed Control for a DC Motor Using Fuzzy logic Controller and Optimal LQR Controller

Comparison of Performance Measures of Speed Control for a DC Motor Using Fuzzy logic Controller and Optimal LQR Controller

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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Title: Improve DC Motor System using Fuzzy Logic Control by Particle Swarm Optimization in Use Scale Factors

Title: Improve DC Motor System using Fuzzy Logic Control by Particle Swarm Optimization in Use Scale Factors

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.

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Speed Control and Adaptive Tuning of DC Motor using P, PI, and Particle Swarm Optimization Techniques

Speed Control and Adaptive Tuning of DC Motor using P, PI, and Particle Swarm Optimization Techniques

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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Vector Control of Induction Motor Using ANN and Particle Swarm Optimization

Vector Control of Induction Motor Using ANN and Particle Swarm Optimization

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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Speed Control of DC Motor Using Particle Swarm Optimization Technique by PSO Tunned PID and FOPID

Speed Control of DC Motor Using Particle Swarm Optimization Technique by PSO Tunned PID and FOPID

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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Particle Swarm Optimization Based Direct Torque Control of Brushless DC Motor

Particle Swarm Optimization Based Direct Torque Control of Brushless DC Motor

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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A fuzzy logic controller for optimal structural control using MR dampers and particle swarm optimization

A fuzzy logic controller for optimal structural control using MR dampers and particle swarm optimization

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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Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

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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Adaptive Tabu Search Optimization Applied on Fuzzy Logic DC Motor Speed Control

Adaptive Tabu Search Optimization Applied on Fuzzy Logic DC Motor Speed Control

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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Application of Fuzzy Logic to the Speed Control of DC Motor

Application of Fuzzy Logic to the Speed Control of DC Motor

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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Dc motor speed control and performance analysis between pid  controller, pi controller and fuzzy logic controller

Dc motor speed control and performance analysis between pid controller, pi controller and fuzzy logic controller

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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IMPLEMENTATION OF SPEED CONTROL OF MOTOR USING FUZZY LOGIC ON FPGA

IMPLEMENTATION OF SPEED CONTROL OF MOTOR USING FUZZY LOGIC ON FPGA

Fuzzy controller in VHDL was implemented due to the need for an inexpensive hardware implementation of a generic fuzzy controller for use in industrial and commercial applications. A very simple fuzzy controller is used to demonstrate this implementation. In the controller, an external device's information, such as that from a sensor, etc., is converted into an output control signal to drive a device(s) such as motors, actuators etc., via the process of fuzzification, rule evaluation and defuzzification. These processes are all based on a set of membership functions and the details of this process can be found in numerous publications.
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REAL TIME IMPLEMENTATION OF A FUZZY LOGIC CONTROLLER ON FPGA USING VHDL FOR DC MOTOR SPEED CONTROL

REAL TIME IMPLEMENTATION OF A FUZZY LOGIC CONTROLLER ON FPGA USING VHDL FOR DC MOTOR SPEED CONTROL

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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Speed Control of Induction Motor using Fuzzy Logic Controller

Speed Control of Induction Motor using Fuzzy Logic Controller

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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Speed Control of Induction Motor using Fuzzy Logic Approach

Speed Control of Induction Motor using Fuzzy Logic Approach

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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Efficiency Optimization technique for Induction Motor with implementation of Fuzzy Logic

Efficiency Optimization technique for Induction Motor with implementation of Fuzzy Logic

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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An Optimal Fuzzy Logic Guidance Law using Particle Swarm Optimization

An Optimal Fuzzy Logic Guidance Law using Particle Swarm Optimization

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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Speed Control of DC Motor by Programmable Logic Control with High Accuracy

Speed Control of DC Motor by Programmable Logic Control with High Accuracy

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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Particle Swarm Optimization based Fuzzy Logic Controller for Luo Converter

Particle Swarm Optimization based Fuzzy Logic Controller for Luo Converter

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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