Top PDF Comparative Study Between Pid Controller And Fuzzypid Controller For Speed Control Of Pm Bldc Motor Drive

Comparative Study Between Pid Controller And Fuzzypid

Controller For Speed Control Of Pm Bldc Motor Drive

Comparative Study Between Pid Controller And Fuzzypid Controller For Speed Control Of Pm Bldc Motor Drive

Nowadays, as a technology grows up, recent development in intelligent control system has emphasized the possibility to integrate the conventional PID controller with an intelligent controller such as FLC. However, PID controller remains a very valid approach to implementing a feedback control system. When the nonlinearities of the system are the major issues in motor control, it could be more convenient to improve the PID control strategy rather than to work out complicated dynamic models which require sophisticated control strategies. One of the factors that contribute to the nonlinearity of BLDC motor is the effects of magnetic saturation as well as reluctance variations. PID parameters need to be tuned based on the system environment. Thus, implementation of FuzzyPID controllers is also less complicated than the implementation of optimization algorithms of PID controller. Regardless of all the existing work, it appears that a detailed comparative analysis of the speed performance under PID and FuzzyPID controller, based on simulation and experiment, has not been done so far. The goal of this research is to provide such a comparison for a variety of operating conditions.
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A Comparative Study of PID and Fuzzy Controller for Speed Control of Brushless DC Motor Drive

A Comparative Study of PID and Fuzzy Controller for Speed Control of Brushless DC Motor Drive

The Brushless dc motors are slowly replacing the DC motors and the AC motors because of their small size, high power factor, high operating speed, high efficiency, less maintenance and excellent speed torque characteristics. They virtually require no repairs, have long life, high dependability, low inertia and friction, and they also have quicker acceleration and can run at superior speed. The disadvantage is its higher cost and it is because of its complicated electronic speed controllers.

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Comparative Study of P, PI and PID Controller for Speed Control of VSI-fed Induction Motor

Comparative Study of P, PI and PID Controller for Speed Control of VSI-fed Induction Motor

Everyone recognizes the vital role played by electrical motors in the development of industrial systems. Induction motors are today a standard for industrial electrical drives and high performance variable speed drive application have a series of advantages. There are several ways for speed control of IMs fed through the VSIs using different modulation techniques. Researchers, scientist and engineers are continuously inventing the new techniques and methods that cover the speed control requirements of the drive. Advanced control techniques such as fuzzy, neuro-fuzzy, genetic algorithm, sliding mode control, etc. have also been extensively used in motion control applications. Inherently straightforward operating characteristics, flexible performance and efficiency encouraged the use of VSI-fed induction motors in many types of industrial drive application. Most multi-purpose production machines benefit from adjustable speed control, since frequently their speeds must change to optimize the machine process or adapt it to various tasks for improved product quality, production speed.
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IMPLEMENTATION AND PERFORMANCE ANALYSIS OF BLDC MOTOR DRIVE BY PID, FUZZY AND ANFIS CONTROLLER

IMPLEMENTATION AND PERFORMANCE ANALYSIS OF BLDC MOTOR DRIVE BY PID, FUZZY AND ANFIS CONTROLLER

satisfactory reaction and stable transition, reaction, but not the optimal response. In the vast majority of literary studies it was assumed that the parameters of the system will never show signs of changes in operating conditions, but in the parameters of the pragmatic application of mechanical stress, such as peace and friction can change due to inactivity or decoupling of the clutch components and load changes. The servo BLDC phase resistance may also vary slightly due to the variation of the terminal resistance of the wiring resistance and the semiconductor resistance due to temperature changes in the operating conditions. It was found that the ratio of the contactless battery at full load is 1:15, and the change in the instantaneous image delay is 10-20 times of the delay components for the decoupling or regular movement of automation control and the Positioning of basic weakness of the conventional controllers is that they can give a better transient and consistent state response when the parameters of the system for which they are planned are kept unchanged. In large part, the sensitive system of the parameters of the systems is changed during the operation. The realization of these controllers and their rationality for a wide range of servo motor drive BLDC studied under different operating conditions, such as changing the species of reference speed parameters and load influencing disturbing. The information regarding the various literary material for the purposes of this study as follows.
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Speed Control of BLDC Motor Drive for Various Applications

Speed Control of BLDC Motor Drive for Various Applications

Abstract:- This paper exhibits a simplified way of controlling of the Permanent Magnet Brushless DC Motor (BLDC) using PI Controller with the comparative study & detailed performance analysis for different load applications in MATLAB environment using Sim Power System (SPS) toolboxes and Simulink in Discrete timespan. A various industrial application such as conveyors, pump load, elevators, lift, etc., in different operating condition like staring mode dynamics, braking mode dynamics, the step change in speed command, etc., have been tested. The choice of BLDC Motor has many advantages over brushed DC motors and induction motors which is of higher speed ranges, long life span, good speed versus torque characteristics, high dynamic response, long operating life, high efficiency, and noiseless operation.
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Model based design of PID controller for BLDC motor with implementation of embedded arduino mega controller

Model based design of PID controller for BLDC motor with implementation of embedded arduino mega controller

The approach used in model based design is to build the abstract representation (model) of application in a modeling language and then obtain the implementation in a programming language automatically using code generator (Wakankar et al. 2010). Model-Based Design is a process that enables fast and cost-effective development of dynamic systems, including control systems, signal processing, and communications systems. In Model-Based Design, a system model is at the center of the development process, from requirements development through design, implementation, and testing (Matlab 2012).The simulation-based approach offers a better understanding of design alternatives and trade-offs than traditional hardware prototype-based design methodologies, enabling to optimize the design to meet predefined performance criteria. Rather than using complex structures and extensive software code, designers can define models with advanced functional characteristics using continuous time and discrete time building blocks. Existing C code can be integrated with standard control library blocks to maximize design efficiency.
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Model based design of pid controller for 
		bldc motor with implementation of embedded arduino mega controller

Model based design of pid controller for bldc motor with implementation of embedded arduino mega controller

In these works, the Model-Based Design approach in development of BLDC Motor PID controller using low cost embedded Arduino Mega controller has been presented. The comparison between simulated and real-time obtained data shows that the motor speed in actual implementation is not significantly different than the simulation result. However, both response are able to track their given input command and are acceptable. Performance indices using Root Mean Squared Error are evaluated and tuned PID controller compared between simulation and actual motor speed response. This work clearly demonstrates that Model-Based Design method which includes task of modeling, control design and rapid- prototyping of designing control system can be easier performed in MATLAB/ Simulink environment using any supported embedded microcontroller with their simulink block-set. The advantages of using Arduino controller with Simulink Arduino Target is an inexpensive, open- source microcontroller board and allows the creation of applications in the Arduino platform based on a visual programming environment with block diagrams. Furthermore, this method is feasible to the development of complex control system design such as artificial intelligence and controller optimization.
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Regulating Speed PFC Bridgeless BuckBoost Converter Fed BLDC Motor Drive With Fuzzy Controller

Regulating Speed PFC Bridgeless BuckBoost Converter Fed BLDC Motor Drive With Fuzzy Controller

The need of the brushless direct current (BLDC) motor in these applications is becoming very common due to features of high efficiency, high flux density per unit volume, less maintenance requirements, and least electromagnetic-interference problems. Efficiency and cost are the main concerns in the growth of low-power motor drives targeting domestic applications such as fans, water pumps, blowers, mixers, etc. These BLDC motors are not controlled to household requirements, but these are suitable for other applications such as medical equipment, transportation, HVAC, motion control, and many industrial tools. A BLDC motor has three phase windings on the stator and permanent magnets on the rotor. an electronic commutation based on rotor position is used rather than a mechanical commutation so it is called as an electronically commutated motor. Because it avoids disadvantages like wear and tear and sparking of brushes and commutator assembly, Power quality problems have become important issues to be considered due to the recommended Control of harmonics in supply current by various international powerqualitystandards.
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On replacing PID controller with ANN controller for DC motor position control

On replacing PID controller with ANN controller for DC motor position control

In order to control the parameters of industrial processes, there are various kinds of actuators on field. Actuator is a driver that runs some mechanical activity. For example, if a process needs to open a valve for fluid motion or move a robotic arm for some appropriate action, there will be a motor with specific applied controls such as the speed and angular position control. DC (Direct Current) motors are often used in various industrial applications where a wide range of responses are required to follow a predetermined trajectory of speed or position under variable load (Faramarzi & Sabahi, 2011). There are various types of control mechanisms that may be applied on the speed and angular position of a DC motor, depending upon the accuracy required.
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Online Tuning Based Fuzzy Logic Controller for Speed Control of BLDC Motor

Online Tuning Based Fuzzy Logic Controller for Speed Control of BLDC Motor

Figure 8.1 shows that the PI controller has a larger overshoot. Figure 8.2 shows that the PID controller does not meet the required speed specifications. The response of the Fuzzy Logic Controller shown in figure 8.3 has a much lesser overshoot and a very good steady state response. The value of the settling time for 2% tolerance for the three controllers are provided in the table 8.1.

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4. SPEED DRIVE SWITCHED RELUCTANCE MOTOR USING PID FUZZY LOGIC CONTROLLER

4. SPEED DRIVE SWITCHED RELUCTANCE MOTOR USING PID FUZZY LOGIC CONTROLLER

A major issue in this research was high ripple content, less steady state error and fast response capability of SRM drive. The proposed controller is used to overcome the issue and to reduce torque ripple content of SRM drive for various speeds. The simulated results are obtained using fuzzy logic controller and hardware to be implemented.

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Modeling and Control of Three Phase BLDC Motor using PID and Fuzzy Logic Controller

Modeling and Control of Three Phase BLDC Motor using PID and Fuzzy Logic Controller

© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2057 Driving circuitry consists of three phase power convertors as shown in Fig. 3, which utilize six power transistors to energize two BLDC motor phases concurrently. The rotor position, which determines the switching sequence of the MOSFET transistors, is detected by means of 3 Hall sensors mounted on the stator.

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PID Fuzzy Logic Controller System for DC Motor Speed Control

PID Fuzzy Logic Controller System for DC Motor Speed Control

Perencanaan perangkat keras meliputi rangkaian minimum sistem DT51 buatan IE (Innovative Electronics), yang dilengkapi dengan mikrokontroler AT89C51, EEPROM AT28C64 sebesar 8 Kbyte, PPI 8255, serta sebuah serial port RS-232, kemudian rangkaian DAC0808, rangkaian PWM (Pulse Width Modulation), rangkaian FtoV (Frekwensi to Voltage), dan rangkaian ADC0804 seperti terlihat pada Gambar 7. Dalam perencanaan kendali Hybrid PID - logika fuzzy, sistem utama tetap menggunakan kendali PID, sedangkan logika fuzzy disini berfungsi untuk memperbaiki respon, dan mempercepat recovery time terhadap disturbance [10].
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Speed Control of Brushless DC Motor based on Fractional Order PID Controller

Speed Control of Brushless DC Motor based on Fractional Order PID Controller

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

Implementation of FPGA based PID Controller for DC Motor Speed Control System

The experimental studies are carried out to evaluate the performance of the controller. Configuration is the Process by which the bit streams of a design, as generated by the development software are loaded into the internal configuration memory of the FPGA. To verify the performance of the controller design on Hardware, the VHDL code (Bit file) is downloaded into the Target FPGA device (Spartan3 family XC3S400) and the complete system is reset. The set speed is assigned to switches according to the requirement and the capture control switch is enabled. Once this is done the ADC data will be read and PID equation implemented will calculate the equivalent PID value and it is fed back to the motor through DAC and once the current speed equals the set speed, the motor starts running at the set speed. Again to change the set speed, the above procedure is repeated by changing the toggle switch position.
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Torque Control of BLDC Motor Using ANFIS Controller

Torque Control of BLDC Motor Using ANFIS Controller

A systematic approach of achieving the speed control of brushless dc motor by means of adaptive neuro fuzzy inference control system has been investigated in this paper. Simulink model was developed in Matlab with the ANFIS controller for the torque control of BLDCM. The control strategy was also developed by writing a set of 9 fuzzy rules according to the ANFIS control strategy with the back propagation algorithm in the back end. The main advantage of designing the ANFIS coordination scheme is to increse the performance of the BLDCM & to increase the dynamic Performance, Simulations were run in Matlab & the results were observed on the corresponding scopes. The outputs take less time to stabilize, which can be observed from the simulation results. Due to the incorporation of the ANFIS controller in loop with the plant, it was observed that the motor reaches the rated speed very quickly in a lesser time compared to the Mamdani method.
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SPEED CONTROL OF SEPERATELY EXCITED DC MOTOR USING ADAPTIVE PID CONTROLLER

SPEED CONTROL OF SEPERATELY EXCITED DC MOTOR USING ADAPTIVE PID CONTROLLER

In this part, some simulation is carried out for MRAPIDC separately excited DC motor controller. Matlab software is used for the simulation of control systems. Figure 3 shows the Simulink models for both MRAPIDC along with the motor under control. The parameters of separately excited DC motor are considered as:

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Speed controller of bldc motor using dspic30f4011 processor approach

Speed controller of bldc motor using dspic30f4011 processor approach

Traditionally inexpensive analog components are used for the design purposes of Motor drives. The weakness of analog systems is their susceptibility to temperature variations and component aging. Another drawback is the difficulty of upgrading the systems. Digital control structures eliminate drifts and, by using a programmable controller, the upgrades can be easily accomplished by software. The high performance of digital signal controllers allows them to perform high-resolution control and minimize control loop delays. These efficient controls make it possible to reduce torque ripples, harmonics and improve dynamic behavior in all speed ranges. The motor design is optimized due to lower vibrations and lower power losses such as harmonic losses in the rotor [2]. Moreover designers have recognized the opportunity to redesign existing systems to use advanced algorithms. For improved efficiency and torque performance, brushless DC (BLDC) motors require a phase advance circuit. Because of the problem of controlled phase advance in BLDC motor we need digital control methodology instead of conventional analog control for the speed control of PMBLDCM. By applying direct digital control it will substantially increase the effective speed range and facilitates a constant power profile. Here the DSP controller is designed to meet the needs of control-based applications of Brushless DC Motor. BLDC motors are becoming popular in Aerospace applications due to better speed v/s torque characteristics, higher efficiency, and high power to frame size, silent operation and reliability.
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Fuzzy Approximation Model-based Robust Controller Design for Speed Control of BLDC Motor

Fuzzy Approximation Model-based Robust Controller Design for Speed Control of BLDC Motor

Hall Effect sensors were used for low cost, low resolution requirements and optical encoder for high resolution requirements [4]. Sensor signals are used to adjust PWM sequence of 3-phase bridge inverter . In sensor-less control back-emf sensing, back-emf integration, flux linkage- based, freewheeling diode conduction and speed independent position function technique are used for electronic commutation [5]. Due to electronical commutation, BLDC has more complex control algorithm compared to other motor types [3, 5]. In practice, the design of the BLDCM drive involves a complex process such as modeling, control scheme selection, simulation and parameters tuning etc. Recently, various modern control solutions are proposed for the speed control design of BLDC motor [1-3]. However, Conventional PID controller algorithm is simple, stable, easy adjustment and high reliability. Conventional speed control system used in conventional PID controller. But, in fact, most industrial processes have been with different degrees of nonlinear, parameter variability and uncertainty of mathematical model of the system. Tuning PID control parameters is very difficult due to its poor robustness; therefore, it is difficult to achieve the optimal state under field conditions in the actual production [6]. So far, there have been many different design methods and control schemes to overcome the uncertain nonlinear control problems such that neural network control system has a strong ability to solve the structure uncertainty but it requires more computing capacity and data storage space. For genetic algorithms, ant-colony algorithms, techniques can help improving performance but they also need longer computation time and larger storage capacity [7-11].
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Comparison of Speed Control of DC Motor Using PID Controller and Optimization Techniques

Comparison of Speed Control of DC Motor Using PID Controller and Optimization Techniques

Tuning a control loop is the adjustment of its control parameters (proportional gain Kp, integral gain Ki and derivative gain(Kd) to the optimum values for the desired control response. There are several methods for tuning a PID loop.[6] The most effective methods generally involve the development of some form of process model, then choosing P, I, and D based on the dynamic model parameters. The choice of method will depend largely on whether or not the loop can be taken "offline" for tuning, and the response time of the system. If the system can be taken offline, the best tuning method often involves subjecting the system to a step change in input, measuring the output as a function of time, and using this response to determine the control parameters. Different tuning methods are[12]
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