Abstract—This paper presents a power factor corrected (PFC) bridgeless (BL) buck–boost converter-fed brushless direct current (BLDC) motordrive as a cost-effective solution for low-power applications. An approach of speed control of the BLDC motor by controlling the dc link voltage of the voltage source inverter (VSI) is used with a single voltage sensor. This facilitates the operation of VSI at fundamental frequency switching by using the electronic commutation of the BLDC motor which offers reduced switching losses. A BL configuration of the buck–boost converter is proposed which offers the elimination of the diode bridge rectifier, thus reducing the conduction losses associated with it. A PFC BL buck–boost converter is designed to operate in discontinuous inductor current mode (DICM) to provide an inherent PFC at ac mains. . In this work, conventional PI and fuzzylogiccontrollers have been used for the speed control of BLDC motordrive. The performance of conventional PI and Fuzzycontrollers are compared under variable reference speed and varying supply voltages with improved power quality at ac mains. The obtained power quality indices are within the acceptable limits of international power quality standards such as the IEC 61000-3-2. The performance of the proposed drive is simulated in MATLAB/Simulink environment
Recently, substantial research efforts have also been devoted to intelligent controllers such as artificial neural networks (ANN) and fuzzylogic to deal with the problems of nonlinearity and uncertainty of system parameters. The fundamental characteristics of neural networks are: ability to produce good models of nonlinear systems; highly distributed and paralleled structure, which makes neural-based control schemes faster than traditional ones; simple implementation by software or hardware; and ability to learn and adapt to the behavior of any real process. On the other hand it was shown that fuzzycontrollers are capable of improving the tracking performance under external disturbances, or when the IFO drivesystem experiences imperfect decoupling due to variations in the rotor time constant. Neural network and fuzzylogic are gaining potential as estimators and controllers for many industrial applications, due to the fact that they posses better tracking properties than conventionalcontrollers.
use of multi-variable control structure. Most of these controllers use mathematical models and are sensitive to parametric variations. Very few adaptive controllers have been practically employed in the control of electric drives due to their complexity and inferior performance. Fuzzycontrollers[8-10] have proved to be successful in recent years. These controllers are inherently robust to load disturbances. Besides, fuzzylogiccontrollers can be easily implemented. The drivesystem considered here consists of fuzzylogic controller, conventional controller, PMBLDCmotor and MOSFET based inverter. All these components are modelled and integrated for simulation. The simulation results shows Fuzzylogic controller has great improvement in both transient and steady state responses of the drive. Contrary to the PI controller, Fuzzylogic controller makes the PMBLDCdrive more robust to load variations. The key feature of this scheme is to compensate the oscillations and harmonics in the response of the PMBLDCmotor. Results of this scheme are compared based on transient analysis and performance measures such as IAE,ISE,THD and Three phase Instantaneous power. Fig1 describes the basic building blocks of the PMBLDCmotor. Section I discusses the basis of three Phase inverter and mathematical modeling of PMBLDCmotor and Inverter. Section II discusses simulation results of PI fed gate control PMBLDCmotordrive. Section III presents the simulation results of Fuzzy control gate method fed PMBLDCmotordrive. Conclusion and references are given at the end.
Circuit model of closed loop controlled induction motordrivesystem is shown in fig. 5.1. The output AC voltage is sensed and compared with reference voltage. The error generated is given to the PI controller to regulate the output voltage with respect to input voltage. The speed response of the closed loop system is shown in fig. 5.3. It seems that speed of the motor increases and then reduces to set value. The Torque variation is shown in fig.8 Line voltage and current waveform are shown .This shows that closed loop system is able to regulate the speed.
Abstract- In this paper Induction motors are the most important workhorse in industries and they are manu- factured in large numbers .The induction motors have mainly developed in constant speed motor drives for general purpose application. The motordrivesystem comprises a voltage source inverter-fed induction motor (VSIM): namely a three-phase voltage source inverter and the induction motor. The squirrel-cage induction motor voltage equations are based on an orthogonal d-q reference rotating frame where the coordinates rotate with the controlled source frequency. The paper presents a novel fuzzylogic controller for closed loop Volts/Hz induction motordrivesystem. Fuzzylogic is a part of artificial intelligence(AI), which is an important branch of computer science or computer engineering. The inputs to the fuzzylogic controller are the linguistic variables of speed error and change of speed error, while the output is change in switching control frequency of the voltage source inverter. In this paper a comparison between fuzzylogic controller and traditional PI controllers are presented. The results validate the robustness and effectiveness of the proposed fuzzylogic controller for high performance of induction motordrive. Simulink software that comes along with MATLAB was used to simulate the proposed model.
An induction or asynchronous motor is an AC motor in which all electromagnetic energy is transferred by inductive coupling from a primary winding to a secondary winding, the two windings being separated by an air gap. In three-phase induction motors, that are inherently self-starting, energy transfer is usually from the stator to either a wound rotor or a short-circuited squirrel cage rotor. Three-phase cage rotor induction motors are widely used in industrial drives because they are rugged, reliable and economical. Single-phase induction motors are also used extensively for smaller loads. Although most AC motors have long been used in fixed-speed load drive service, they are increasingly being used in variable-frequency drive (VFD) service, variable-torque centrifugal fan, pump and compressor loads being by far the most important energy saving applications for VFD service. Squirrel cage induction motors are most commonly used in both fixed-speed and VFD applications. Usage of induction motors reminds us to develop a better control over it. This induction motors have the advantage of decoupling (separation) of the torque and flux control which makes high servo quality achievable. [17, 22, 30] Torque and flux parameters are responsible for generating rotating motion of rotor. These parameters are affected depending on the load disturbances. The decoupling control feature can be adversely affected by load torque disturbances and parameter variations in the motor. This instantly lowers the speed down compared to the desired speed so that the variable-speed tracking performance of an Induction motor is degraded. In order to attain the rated speed there are many controllers like conventional PI controller. A proportional-integral controller (PI controller) is a generic control loop feedback
The interior permanent magnet synchronous motor (IPMSM) is arguably the best choice for high performance variable speed drives (HPVSD). But its precise speed and torque control appear to be a complex task for researchers due to nonlinear coupling among its winding currents and the rotor speed, as well as the nonlinearity present in the electromagnetic developed torque because of magnetic saturation of the rotor core. The most tangible option for researchers is to employ simple fixed gain PI, PID controllers which provide good steady state performance but suffer from poor dynamic performance, sensitivity to parameter variations and occasional instability. Meanwhile, the conventional adaptive controllers require complex circuitry for real-time implementation. Artificial intelligent controllers like fuzzy, neural network, neuro-fuzzycontrollers are good options in this case because they are capable of handling nonlinear systems without much knowledge of the system model and also they yield better transient speed response. But most often, researchers overlook the possibility of simultaneous torque ripple control while achieving better dynamic speed response. So in this thesis, a new IPMSM drive has been proposed with a combined approach of optimizing torque ripple and achieving better dynamic speed performance over a wide speed range.
This project presents a performance of the canonical switching cell (CSC) device fed brushless DC (BLDC) motordrive for power quality (PQ) improvement. The employment of CSC not solely controlled the DC link voltage however additionally create the inverter to control at low frequency in order that switching losses are reduced. Furthermore the utilization of front end CSC improves the power factor at AC mains. A design methodology is introduced that blends the classical fuzzylogiccontrollers in associate degree intelligent means and so a new intelligent controller has been achieved. Moreover, associate degree intelligent switching pattern is evoked on the mixing mechanism that produces a choice upon the priority of the two controller parts; particularly, the classical PI and also the fuzzy constituents. The simulations done on varied processes using the new fuzzy controller provides ‘better’ system responses in terms of transient and steady-state performances in comparison to the pure classical PI or the pure fuzzy controller applications. The performance graph has been plotted for the total harmonic distortion (THD) and also the power factor (PF). A front end Canonical switching cell device operating in Discontinuous inductor Current Mode (DICM) is planned for power factor correction operation at AC mains. fuzzylogic is introduced so as to suppress the chattering and enhancing the hardiness of the PFC control system. The performance has been evaluated with the help of Mat lab-Simulink.
This paper presents two controllers for implementing the current multiplier approach over a wide range of speed control of a Brushless DC (BLDC) Motordrivesystem as a cost effective low-power solution. A CUK converter at the front end feeds the DC bus of the Voltage Source Inverter (VSI), where, closed loop duty ratio control of the converter results in variable DC bus voltage, enabling close matching of the reference speed setting. Two alternate controller configurations viz. PI controller and fuzzy controller are introduced for generating gate trigger signals for the power MOSFET switch of the converter. Comparison of performance covering a over a wide range of operating speed of the entire system using PI/FUZZYcontrollers is carried out by simulation in MATLAB/Simulink platform.
ABSTRACT: Permanent Magnet Brushless Direct Current (PMBLDC) motors are widely accepted for their high efficiency, reliability, good dynamic response and low maintenance. This paper proposes an effective speed control of PMBLDCmotordrive using conventional Cuk converter and bridgeless Cuk converter. Further a performancecomparison is made using PI and fuzzycontrollers for the speed control of PMBLDCmotordrive in both the converters. The simulation and performancecomparison is analysed using MATLAB/SIMULINK software.
A single stage PFC control strategy of a VSI fed PMBLDCM drive using power quality converter using conventional PI controller and fuzzylogic controller has been validated. As a conclusion, the increasing demand for using fuzzylogic as a controller for BLDC permanent magnet motor in modern intelligent motion control of BLDC motors, simulation have provided a good dynamic performance of the fuzzylogic controller system. Be sides. The simulation model which is implemented in a modular manner under MATLAB environment allows dynamic characteristics such as phase currents, rotor speed, and mechanical torque to be effectively considered. Also, THD of system is reduced and power factor is improved. The result paired with Matlab/simulink is a good simulation tool for modeling and analyzing fuzzylogic controlled brushless DC motor drives.
Brushless D.C (BLDC) synchronous motors have been used in various fields of industrial applications for their high power/weight, high torque, high efficiency, long operating life, noiseless operation, high speed ranges and ease of drive control . Permanent Magnet Brushless DC (PMBLDC) motor is defined as a permanent magnet synchronous motor with a trapezoidal Back EMF waveform . BLDC motors do not have brushes for commutation. They are electronically commutated . For the variable speed applications of BLDC motor, Proportional, Integral and Derivative (PID) motor control is commonly used control .Because; it has simple design and ease of control. However, its performance depends on proportional, integral and derivative gains [5- 6]. When the operating condition changes, the re-tuning process of control gains is necessary for dynamically minimize the total controller error. The various algorithms are used to find optimal PID controller parameters such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) [7-10].Particle Swarm Optimization (PSO) and genetic algorithm (GA) is given based on population size, generation number, selection method, and crossover and mutation probabilities. There is no guarantee for finding optimal solutions for controllers within a finite amount of time. To overcome the problems in PID controller, fuzzylogic controller and hybrid fuzzy PID controllers can be designed for the speed control of BLDC motor. In this proposed research work, the speed control of BLDC motor was analyzed and its performance has been observed by using fuzzylogic controller and hybrid fuzzy PID [11- 13].The simulation results of two methods are studied and compared with conventional PI controller by using MATLAB/SIMULINK computational software. The simulation results of proposed controllers are used to show the abilities and shortcomings of conventional PI controller.
Abstract- This paper presents the modeling, simulation, and speed control aspects of a 3-phase 6/4 Switched Reluctance Motor (SRM) drives, using hybrid Artificial Intelligence FuzzyLogic Controller system. Also a speed control design for Switched Reluctance Motordrive based on fuzzylogic controller is suggested. The fuzzy controller is proposed in this paper as speed controller for SRM. The whole control mechanism consists of a detailed report about the steady state and transient analysis of Switched Reluctance Motor. The control design results are then validated in real-time by Simulink / Matlab software package. The main aim of this project is to control the speed of the Switched Reluctance Motor very effectively using FuzzyLogic Controller. Though PI controller is more popular and widely used, Fuzzy is something which is more advanced and efficient when compared to other conventionalcontrollers.
The ripple contents of stator current, electromagnetic torque and rotor speed are minimized with FLC method. The advantages of FuzzyLogic Controller is that it does not require any mathematical model and only based on the linguistic rules.The use of the d-q-0 reference frame for BLDCM is based on the fact that, in a three-phase Y-connected motor with non- sinusoidal air gap flux distribution, the d-q-0 transformation of the three line-to-line back EMF‟s results in the finding of the d- and q- components identical to those of three phase back EMF‟s transformation.
Similarly, in many material handling systems, adjustable speed drives can increase system efficiency and improve system reliability. For example, in many conveyor systems, lines are controlled by energizing and de-energizing a series of motors. These frequent starts and shutdowns are tough on motors and line components because of repeated stresses from starting currents and acceleration and deceleration of mechanical components. Using variable speed drives can smooth out line motion for more efficient and effective operation. Some motors have inherent speed control capabilities. For example, dc motors have excellent speed and torque control characteristics and are often used when high torque at low speeds is required. The speed adjustments of dc motors can be as much as 20:1, and they can operate at 5% to 7% of the motor’s base speed (some can even operate at 0 rpm). Some ac motors can also be used in speed adjustment situations. Wound rotor motors can have speed ratios of as much as 20:1 by changing the resistance in the rotor circuit. Another common method of controlling speed is to use induction motors combined with VFDs. Induction motors are widely used in industrial applications because of their inherent advantages in terms of cost, reliability, availability, and low maintenance requirements. Mechanics and
The electric drivesystem is a vital part to drive any motor. The electric drivesystem is used to control the position, speed and torque of the electric motors. Many works has been done on power converter topologies, control scheme of the electric drive systems and on the motor types in order to enhance and improve the performance of the electric motors so as to exactly perform and do what is required . Induction Motors (IMs) are widely used in industrial, commercial and domestic applications as they are simple, rugged, low cost and easy to maintain. Since IMs demands well control performances: precise and quick torque and flux response, large torque at low speed, wide speed range, the drive control system is necessary for IMs .
ABTRACT: Brushless Direct Current (BLDC) motors are widely used due to high reliability, simple frame, straight forward control, and low friction. BLDC motor has the advantage of high speed adjusting performance and power density. Speaking of the motordrive, the most important part is commutation control. On the other hand, they show a high torque ripple characteristics caused by nonideal commutation currents. This limits their application area especially for the low-voltage applications. In order to minimize torque ripple for the entire speed range, a comprehensive analysis of commutation torque ripple was made according to phase advancing(PA) commutation control method. This approach is based on the terminal voltage sensing and converting the voltages into d-q reference frame and the commutation signals are generated by comparing it with reference values. The gating signals are obtained by switching sequence of BLDC motor and it is done using fuzzylogic controller(FLC).The design analysis and simulation of the proposed system is done using MATLAB version 2013a and the simulation results of proportional- integral (PI) controller and fuzzylogic controller(FLC) method is compared.
Initially results are presented which demonstrate that the ANN can estimate the derivative of any vector required for position estimation (first null, first active, second active or second null) in the PWM waveform to a reasonable accuracy. To facilitate this, the PM machine was operated as a normal closed loop vector controlled drive, obtaining a position measurement from a shaft mounted encoder. The estimated quantities were not used to control the motor (hence this testing is referred to as open loop). A 17µs minimum pulse width was applied to the PWM vectors allowing measurements from the Rogowski coil and 2IS method to be taken for comparison. The speed and load were set to values used when capturing ANN training data (30Hz, 83% load) to demonstrate the behaviour at what should be a good operating point. Fig. 2 (a)-(d) show the derivative estimates for each of the PWM vectors where one fundamental period is represented by 167 samples on the x-axis.
Figure 9 shows the SRM Speed of Fuzzylogic controller. Initially stating Speed of 6900rpm and after 0.05sec finally speed is settled at 4500 rpm. The PI-controller takes decision during steady state to reduce steady state error of the system and the fuzzylogic controller takes decision during transient state to get fast response and low overshoot when the absolute value of speed error is greater than 7 rpm. This set value depends upon the fuzzylogic controller and the sampling frequency of for the case of steady state, the PI-controller dominates the control output to significantly reduce steady state error of the system and the FLC contributes to the output to provide fast response and low overshoot when the absolute value of speed error is higher than 7 rpm.