A comparative study of pi, fuzzy and hybrid pi fuzzy controller for speed con...Asoka Technologies
This paper presents the comparative study between PI, fuzzy and hybrid PI-Fuzzy controller for speed control of brushless dc (BLDC) motor. The control structure of the proposed drive system is described. The simulation results of the drive system for different operation modes are evaluated and compared. A fuzzy controller offers better speed response for start-up while PI controller has good compliance over variation of load torque but has slow settling response. Hybrid controller has an advantage of integrating a superiority of these two controllers for better control performances. Matlab/Simulink is used to carry out the simulation.
Design of Fuzzy Logic Controller for Speed Regulation of BLDC motor using MATLABijsrd.com
Brushless DC (BLDC) motors drives are one of the electrical drives that are rapidly gaining popularity, due to their high efficiency, good dynamic response and low maintenance. The design and development of a BLDC motor drive for commercial applications is presented. The aim of paper is to design a simulation model of inverter fed PMBLDC motor with Fuzzy logic controller. Fuzzy logic controller is developed using fuzzy logic tool box which is available in Matlab. FIS editor used to create .FIS file which contains the Fuzzy Logic Membership function and Rule base. And membership functions of desired output. After creating .FIS file it is implemented in the Matlab Simulink. And the BLDC motor is run satisfactorily using the Fuzzy logic controller.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This paper presents an analysis by which the dynamic performances of a permanent magnet
brushless dc (PMBLDC) motor drive with different speed controllers can be successfully predicted. The
control structure of the proposed drive system is described. The dynamics of the drive system with a
classical proportional-integral-derivative (PID) and Fuzzy-Logic (FL) speed controllers are presented.
The simulation results for different parameters and operation modes of the drive system are investigated
and compared. The results with FL speed controller show improvement in transient response of the
PMBLDC drive over conventional PID controller. Moreover, useful conclusions stemmed from such a
study which is thought of good use and valuable for users of these controllers
A comparative study of pi, fuzzy and hybrid pi fuzzy controller for speed con...Asoka Technologies
This paper presents the comparative study between PI, fuzzy and hybrid PI-Fuzzy controller for speed control of brushless dc (BLDC) motor. The control structure of the proposed drive system is described. The simulation results of the drive system for different operation modes are evaluated and compared. A fuzzy controller offers better speed response for start-up while PI controller has good compliance over variation of load torque but has slow settling response. Hybrid controller has an advantage of integrating a superiority of these two controllers for better control performances. Matlab/Simulink is used to carry out the simulation.
Design of Fuzzy Logic Controller for Speed Regulation of BLDC motor using MATLABijsrd.com
Brushless DC (BLDC) motors drives are one of the electrical drives that are rapidly gaining popularity, due to their high efficiency, good dynamic response and low maintenance. The design and development of a BLDC motor drive for commercial applications is presented. The aim of paper is to design a simulation model of inverter fed PMBLDC motor with Fuzzy logic controller. Fuzzy logic controller is developed using fuzzy logic tool box which is available in Matlab. FIS editor used to create .FIS file which contains the Fuzzy Logic Membership function and Rule base. And membership functions of desired output. After creating .FIS file it is implemented in the Matlab Simulink. And the BLDC motor is run satisfactorily using the Fuzzy logic controller.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This paper presents an analysis by which the dynamic performances of a permanent magnet
brushless dc (PMBLDC) motor drive with different speed controllers can be successfully predicted. The
control structure of the proposed drive system is described. The dynamics of the drive system with a
classical proportional-integral-derivative (PID) and Fuzzy-Logic (FL) speed controllers are presented.
The simulation results for different parameters and operation modes of the drive system are investigated
and compared. The results with FL speed controller show improvement in transient response of the
PMBLDC drive over conventional PID controller. Moreover, useful conclusions stemmed from such a
study which is thought of good use and valuable for users of these controllers
Integrated fuzzylogic controller for a Brushless DC Servomotor systemEhab Al hamayel
This presentation discusses the designing and simulation of "Integrated fuzzylogic controller for a Brushless DC Servomotor system" using Matlab simulink
Speed Control of Brushless Dc Motor Using Fuzzy Logic Controlleriosrjce
This paper presents a control scheme of a fuzzy logic for the brushless direct current (BLDC)
permanent magnet motor drives. The mathematical model of BLDC motor and fuzzy logic algorithm is derived.
The controller is designed to tracks variations of speed references and stabilizes the output speed during load
variations. The BLDC has some advantages compare to the others type of motors, however the nonlinearity of
the BLDC motor drive characteristics, because it is difficult to handle by using conventional proportionalintegral
(PI) controller. The BLDC motor is fed from the inverter where the rotor position and current
controller is the input. In order to overcome this main problem, the fuzzy logic control is learned continuously
and gradually becomes the main effective control. The effectiveness of the proposed method is verified by
develop simulation model in MATLAB-Simulink program. The simulation results show that the proposed fuzzy
logic controller (FLC) produce significant improvement control performance compare to the PI controller for
both condition controlling speed reference variations and load disturbance variations. Fuzzy logic is introduced
in order to suppressing the chattering and enhancing the robustness of the controlled system. Fuzzy boundary
layer is developed to provide smother transition to the equivalent control. Smaller overshoot in the speed
response and much better disturbance rejecting capabilities.
DC MOTOR SPEED CONTROL USING ON-OFF CONTROLLER BY PIC16F877A MICROCONTROLLERTridib Bose
This presentation consists the speed control of a dc motor using hardware (microcontroller) by changing the reference voltages logically and minimising errors.
Simulation DC Motor Speed Control System by using PID Controllerijtsrd
Speed control system is the most common control algorithm used in industry and has been universally accepted in industrial control. One of the applications used here is to control the speed of the DC motor. Controlling the speed of a DC motor is very important as any small change can lead to instability of the closed loop system. The aim of this thesis is to show how DC motor can be controlled by using PID controller in MATLAB. The development of the PID controller with the mathematical model of DC motor is done using automatic tuning method. The PID parameter is to be test with an actual motor also with the PID controller in MATLAB Simulink. In this paper describe the results to demonstrate the effectiveness and the proposed of this PID controller produce significant improvement control performance and advantages of the control system DC motor. Mrs Khin Ei Ei Khine | Mrs Win Mote Mote Htwe | Mrs Yin Yin Mon ""Simulation DC Motor Speed Control System by using PID Controller"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25114.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/25114/simulation-dc-motor-speed-control-system-by-using-pid-controller/mrs-khin-ei-ei-khine
Three-phase ac motors have been the workhorse of industry since the earliest days of electrical engineering. They are reliable, efficient, cost-effective and need little or no maintenance. In addition, ac motors such as induction and reluctance motors need no electrical connection to the rotor, so can easily be made flameproof for use in hazardous environments such as in mines.
In order to provide proper speed control of an ac motor, it is necessary to supply the motor with a three phase supply of which both the voltage and the frequency can be varied. Such a supply will create a variable speed rotating field in the stator that will allow the rotor to rotate at the required speed with low slip. This ac motor drive can efficiently provide full torque from zero speed to full speed, can overspeed if necessary, and can, by changing phase rotation, easily provide bi-directional operation of the motor. A drive with these characteristics is known as a PWM (Pulse Width Modulated) motor drive.
Drives and motors are an integral part of industrial equipment from packaging,robotics, computer numerical control (CNC), machine tools, industrial pumps,and fans. Designing next-generation drive systems to lower operating costs requires complex control algorithms at very low latencies as well as a flexibleplatform to support changing needs and the ability to design multiple-axis systems.
Traditional drive systems based on ASICs, digital signal processors (DSPs), and microcontroller units lack the performance and flexibility to address these needs. Altera’s family of FPGAs provides a scalable platform that can be used to offload control algorithm elements in hardware. You may also integrate the whole drive system with industry-proven processor architectures while supporting multipletypes of encoders and industrial Ethernet protocols. This “drive on a chip” system reduces cost and simplifies development.
Performance analysis of various parameters by comparison of conventional pitc...eSAT Journals
Abstract This paper deals with a variable speed wind turbine coupled with a permanent magnet synchronous generator connected through a two mass drive train. This drive train is connected to synchronous generator and after the conversion process finally connected to grid and the idea of transmission over a long distance makes the use of converter necessary and at the receiving end. The inverter is used to convert it back and the inverter is designed with a proper gate signal to get the best output three phase voltages. The fuzzy logic controller is used to track generator speed with varying wind speed to optimize turbine aerodynamic efficiency in the outer speed loop. Pitch angle control of wind turbine has been used widely to reduce torque and output power variation in high rated wind speed areas .The machine side converter is designed to extract maximum power from the wind. In this work a WECS connected with grid is designed in Matlab and a Fuzzy controller is designed to improve the output and we can see the major difference in DC link voltage and reactive power in transmission line. From the outputs we can also go through the reactive power issue which system is best for inductive load or capacitive load. The simple PI system is good for capacitive load and the fuzzy system is better option for the inductive load. The results of both the system of normal controller and fuzzy controller is compared and analyzed. Key Words: Fuzzy logic controller (FLC), permanent magnet synchronous generator (PMSG), insulated gate bipolar transistor (IGBT) , Pulse width modulation (PWM), Wind energy conversion system, DC link capacitor. FACTS Flexible A.C Transmission system, PI proportional integral
Integrated fuzzylogic controller for a Brushless DC Servomotor systemEhab Al hamayel
This presentation discusses the designing and simulation of "Integrated fuzzylogic controller for a Brushless DC Servomotor system" using Matlab simulink
Speed Control of Brushless Dc Motor Using Fuzzy Logic Controlleriosrjce
This paper presents a control scheme of a fuzzy logic for the brushless direct current (BLDC)
permanent magnet motor drives. The mathematical model of BLDC motor and fuzzy logic algorithm is derived.
The controller is designed to tracks variations of speed references and stabilizes the output speed during load
variations. The BLDC has some advantages compare to the others type of motors, however the nonlinearity of
the BLDC motor drive characteristics, because it is difficult to handle by using conventional proportionalintegral
(PI) controller. The BLDC motor is fed from the inverter where the rotor position and current
controller is the input. In order to overcome this main problem, the fuzzy logic control is learned continuously
and gradually becomes the main effective control. The effectiveness of the proposed method is verified by
develop simulation model in MATLAB-Simulink program. The simulation results show that the proposed fuzzy
logic controller (FLC) produce significant improvement control performance compare to the PI controller for
both condition controlling speed reference variations and load disturbance variations. Fuzzy logic is introduced
in order to suppressing the chattering and enhancing the robustness of the controlled system. Fuzzy boundary
layer is developed to provide smother transition to the equivalent control. Smaller overshoot in the speed
response and much better disturbance rejecting capabilities.
DC MOTOR SPEED CONTROL USING ON-OFF CONTROLLER BY PIC16F877A MICROCONTROLLERTridib Bose
This presentation consists the speed control of a dc motor using hardware (microcontroller) by changing the reference voltages logically and minimising errors.
Simulation DC Motor Speed Control System by using PID Controllerijtsrd
Speed control system is the most common control algorithm used in industry and has been universally accepted in industrial control. One of the applications used here is to control the speed of the DC motor. Controlling the speed of a DC motor is very important as any small change can lead to instability of the closed loop system. The aim of this thesis is to show how DC motor can be controlled by using PID controller in MATLAB. The development of the PID controller with the mathematical model of DC motor is done using automatic tuning method. The PID parameter is to be test with an actual motor also with the PID controller in MATLAB Simulink. In this paper describe the results to demonstrate the effectiveness and the proposed of this PID controller produce significant improvement control performance and advantages of the control system DC motor. Mrs Khin Ei Ei Khine | Mrs Win Mote Mote Htwe | Mrs Yin Yin Mon ""Simulation DC Motor Speed Control System by using PID Controller"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25114.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/25114/simulation-dc-motor-speed-control-system-by-using-pid-controller/mrs-khin-ei-ei-khine
Three-phase ac motors have been the workhorse of industry since the earliest days of electrical engineering. They are reliable, efficient, cost-effective and need little or no maintenance. In addition, ac motors such as induction and reluctance motors need no electrical connection to the rotor, so can easily be made flameproof for use in hazardous environments such as in mines.
In order to provide proper speed control of an ac motor, it is necessary to supply the motor with a three phase supply of which both the voltage and the frequency can be varied. Such a supply will create a variable speed rotating field in the stator that will allow the rotor to rotate at the required speed with low slip. This ac motor drive can efficiently provide full torque from zero speed to full speed, can overspeed if necessary, and can, by changing phase rotation, easily provide bi-directional operation of the motor. A drive with these characteristics is known as a PWM (Pulse Width Modulated) motor drive.
Drives and motors are an integral part of industrial equipment from packaging,robotics, computer numerical control (CNC), machine tools, industrial pumps,and fans. Designing next-generation drive systems to lower operating costs requires complex control algorithms at very low latencies as well as a flexibleplatform to support changing needs and the ability to design multiple-axis systems.
Traditional drive systems based on ASICs, digital signal processors (DSPs), and microcontroller units lack the performance and flexibility to address these needs. Altera’s family of FPGAs provides a scalable platform that can be used to offload control algorithm elements in hardware. You may also integrate the whole drive system with industry-proven processor architectures while supporting multipletypes of encoders and industrial Ethernet protocols. This “drive on a chip” system reduces cost and simplifies development.
Performance analysis of various parameters by comparison of conventional pitc...eSAT Journals
Abstract This paper deals with a variable speed wind turbine coupled with a permanent magnet synchronous generator connected through a two mass drive train. This drive train is connected to synchronous generator and after the conversion process finally connected to grid and the idea of transmission over a long distance makes the use of converter necessary and at the receiving end. The inverter is used to convert it back and the inverter is designed with a proper gate signal to get the best output three phase voltages. The fuzzy logic controller is used to track generator speed with varying wind speed to optimize turbine aerodynamic efficiency in the outer speed loop. Pitch angle control of wind turbine has been used widely to reduce torque and output power variation in high rated wind speed areas .The machine side converter is designed to extract maximum power from the wind. In this work a WECS connected with grid is designed in Matlab and a Fuzzy controller is designed to improve the output and we can see the major difference in DC link voltage and reactive power in transmission line. From the outputs we can also go through the reactive power issue which system is best for inductive load or capacitive load. The simple PI system is good for capacitive load and the fuzzy system is better option for the inductive load. The results of both the system of normal controller and fuzzy controller is compared and analyzed. Key Words: Fuzzy logic controller (FLC), permanent magnet synchronous generator (PMSG), insulated gate bipolar transistor (IGBT) , Pulse width modulation (PWM), Wind energy conversion system, DC link capacitor. FACTS Flexible A.C Transmission system, PI proportional integral
Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC...Francisco Gonzalez-Longatt
DC microgrids are desired to provide the electricity for the remote areas which are far from the main grid. The microgrid creates the open horizontal environment to interconnect the distributed generation especially photovoltaic (PV). The stochastic nature of the PV output power introduces the large fluctuations of the power and voltage in the microgrid and forced to introduce the controller for voltage stability. There are many control strategies to control the voltage of a DC microgrid in the literature. In this paper the proportional-integral-derivative (PID) and fuzzy logic PID (FL-PID) controller has been designed and compared in term of performance. Performance measures like maximum overshoot and settling time of FL-PID compared with the PID proved that the former is better controller. The controllers are designed and simulated in the MATLAB programming environment. The controllers has been tested for the real time data obtained from Pecan Street Project, University of Texas at Austin USA.
Fuzzy Logic
Where did it begin?
What is Fuzzy Logic?
Fuzzy Logic in Control Systems
Fuzzy Logic in Other Fields
Fuzzy Logic vs. Neural Networks
Fuzzy Logic Benefits
Fuzzy logic is often heralded as a technique for handling problems with large amounts of vagueness or uncertainty. Since its inception in 1965 it has grown from an obscure mathematical idea to a technique used in a wide variety of applications from cooking rice to controlling diesel engines on an ocean liner.
This talk will give a layman's introduction to the topic and explore some of the real world applications in control and human decision making. Examples might include household appliances, control of large industrial plant, and health monitoring systems for the elderly. We will look at where the field might be going over the next ten years, highlighting areas where DMU's specialist expertise drives the way.
Advantages and Disadvatages of AC/DC MotorFika Khamis
Simple explanation on advantages and disadvantages of AC and DC motor. Focusing on main point only since the slides is for presentation. Originally design by me.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
A novel speed controller for the three-phase Brushless DC (BLDC) Motor Drive is proposed using a closed-loop AC-DC Bridgeless SEPIC Converter in continuous Conduction mode. This design proposes a single stage AC-DC converter with ON and OFF state equivalent circuits for 400W, 48V at 2450 rpm PMBLDC motor drive. The Fuzzy based voltage and current controlling method is proposed in this design. The voltage controlling method is used to control the speed for BLDC motor and the current controlling method is used to improve the power factor in AC supply. The speed of BLDC motor is observed with voltage disturbance and the constant motor speed is maintained. The proposed control method on SEPIC converter fed PMBLDC motor drive is modeled by Simulink/Matlab.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Efficient bridgeless SEPIC converter fed PMBLDC motor using artificial neural...IJECEIAES
In this paper, a new design of Bridgeless SEPIC (Single Ended Primary Inductance converter) with Artificial neural network (ANN) fed PMBLDC Motor drive is proposed to improve Power Factor. The proposed converter has single switching device of MOSFET, so the switching losses is reduced.ANN is used to achieve the higher power factor and fixed dc link voltage. Also the ANN methodology the time taken for computation is less since there is no mathematical model. The output voltage depends on the switching frequency of the MOSFET. The BLSEPIC act as a buck operation in continuous conduction mode. Detailed converter analysis, equivalent circuit and closed-loop analysis are presented for 36V, 120W, 1500rpm BLDC Motor drive. This proposed converter produces low conduction loss, low total harmonic reduction, low settling time and high power factor reaching near-unity. All the simulation work is verified with MATLAB – Simulink.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
As the technology is fast changing, there is more and more use of machine intelligence in modern motor controllers. These controllers are employed in advanced electric motor drives in particular, the present day Induction motor drives. These systems emulate the human logic. This is particularly useful when the application has poorly defined mathematical model. In this present paper the analysis of fuzzy logic as the artificial intelligence is used. The comparative study of Fuzzy PI, Fuzzy MRAC is made. There is always a compromise of the cost and complexity. So this paper presents a new approach and its dynamic response in comparison to the Fuzzy PI and Fuzzy MRAC. The proposed controller is Fuzzy PI with scaling factors. This approach is validated with the Speed, torque responses of Indirect vector controlled Induction motor (IVCIM) drive.
This work shows the design and tuning procedure of a discrete PID controller for regulating buck boost converter circuits. The buck boost converter model is implemented using Simscape Matlab library without having to derive a complex mathematical model. A new tuning process of digital PID controllers based on identification data has been proposed. Simulation results are introduced to examine the potentials of the designed controller in power electronic applications and validate the capability and stability of the controller under supply and load perturbations. Despite controller linearity, the new approach has proved to be successful even with highly nonlinear systems. The proposed controller has succeeded in rejecting all the disturbances effectively and maintaining a constant output voltage from the regulator.
In this paper, several methods are developed to control the brushless DC (BLDC) motor speed. Since it is difficult to get a good showing by utilizing classical PID controller, the Dynamic Wavelet Neural Network (DWNN) is the proposed work in this paper, with parallel PID controller to obtain an novel controller named DWNN-PID controller. It collects the artificial neural ability of its networks for imparting from motor of BLDC with drive system and the ability of identification for the wavelet decomposition and control of the dynamic system furthermore to have ability for adapting and self-learning. The suggested controller method is utilizing to control the speed of BLDC motor of which supply a better showing than utilizing classical controllers with a wide range of control. The proposed controller parameters are matched continuously using Particle Swarm Optimization (PSO) algorithm. The simulation results based on proposed DWNN-PID controller demonstrate a superior in the stability and performance compared at utilizing classical WNN-PID and conventional PID controllers. The simulation results are accomplished using Matlab/Simulink. It shows that the proposed control scheme has a superior performance.
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Adaptive Fuzzy PID Regulator for the Speed Control of Brushless Dc Motor
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 10, Issue 1 Ver. II (Jan – Feb. 2015), PP 10-17
www.iosrjournals.org
DOI: 10.9790/1676-10121017 www.iosrjournals.org 10 | Page
Adaptive Fuzzy PID Regulator for the Speed Control of Brushless
Dc Motor
Dr. Anitha P1
‘Professor, Department of Electrical & Electronics Engg, NSS College of Engg’
Abstract: This paper presents an intelligent speed control system based on self tuning fuzzy PID controller for
a brushless DC motor.PWM based motor current control is implemented with the help of three hall sensors
placed around the motor shaft and a three phase inverter model is implemented for motor commutation
.Dynamic performance of electromagnetic torque developed are analysed for this model. Traditional speed
control system of BLDCM introduces conventional PI regulator in outer speed loop which debases the
performance of whole system. To overcome this problem, replacement of PI controller by an intelligent
controller based on fuzzy set theory is proposed. The performance of the intelligent controller has been
investigated through Matlab Simulink package for different operating conditions such as sudden changes in
load torque. The simulation results demonstrate that the performance of the proposed controller is better than
that of conventional PI controller.
Keywords: Fuzzy Logic Controllersr, BLDC,PID Controller
I. Introduction
With the rapid developments in power semiconductor technologies and manufacturing technology for
high quality magnetic materials, the BLDC motors have been widely used for various industrial and servo
applications. BLDC motors are reliable, easy to control and inexpensive. Due to their favourable electrical and
mechanical properties ,BLDC motors are widely used in applications such as automotive,aerospace ,medical
,instrumentation, actuation ,robotics and industrial automation equipments.BLDC motors has many advantages
over conventional dc servo motors such as high dynamic response,long operating life ,high efficiency,long
operating characteristics and high torque-weight ratio.The stator of BLDC motors have three phase windings
,whereas rotor has pole magnets.The hall sensors embedded in the motor detects rotor position and decoder
decodes the position of rotor and produces gate pulses to trigger the six pulse inverter to produce ac voltages
that energizes the stator windings.BLDC motors have electronic commutators instead of brushes ,thus they have
higher efficiency, noiseless operation and long operating life. Fuzzy control provides a formal methodology
for representing, manipulating and implementing a human heuristic knowledge about how to control the
performance of a real dynamic system .Some simulation models based on state space equations,analysis of
torque ripple due to phase commutation have been proposed for the analysis of BLDC motor
drives[1],[2].Furthermore fuzzy logic controllers are used to analyse BLDC motor drives in literature[3],[4].
The FLC technique is a model-free techinique and inherently robust to load disturbances with the ease of
implementation[5]. In this paper, a self tuning PID Fuzzy Logic Controller is proposed for the speed control of
BLDC motor.
II. Modelling Of Permanent Magnet Brushless Dc Motor
The complete BLDC motor drive system consists of a permanent magnet motor fed by a three phase
PWM inverter ,hysteresis current controller,rotor position sensor and speed controllerThe phase variables are
used to model the BLDC motor due to its nonsinusoidal back emf and phase current.The controlled power to
motor is supplied by the inverter. The magnitude and frequency of the inverter output voltage depends on the
switching signals generated by the hysteresis controller. The state of these switching signals at any instant is
determined by the rotor position, speed variation and winding currents. The controller facilitates the variable
speed operation of the drive and also synchronizes winding currents with the rotor position. It maintains the
motor speed reference value even during load and supply variations. The Fig.1 shows the configuration of
Permanent Magnet Brushless DC Motor drive system
2. Adaptive fuzzy pid regulator for the speed control of brushless dc motor
DOI: 10.9790/1676-10121017 www.iosrjournals.org 11 | Page
Fig.1 Brushless DC Motor Drive system
The mathematical model of the motor is developed based on the assumption in [4 ]. The terminal
voltage equation of BLDC motor can be written as
Where Va,Vb andVc are the phase voltages,ia,ib,ic are the phase currents,ea,eb and ec are phase back
emf voltages, R is the phase resistance ,L=La=Lb=Lc is the synchronous inductance per phase and includes both
leakages and armature reaction inductances.
The electmro magnetic torque is given by
Ta = eaia+ebib+ecic/wm (2)
Where ‘wm’ is the mechanical speed of motor.
III. Design Of Adaptive Fuzzy Pid Controller
The membership functions for controller input error and change in error are defined on the common
interval [-1 1]. The membership function for gain updating factor is defined on [0 1]. The triangular
membership function is selected with fifty percent overlap except at the two membership functions at the
extreme end. The membership functions for error and change in error are shown in Fig. 2 and Fig.3
respectively. Their control output is shown in Fig. 4. The membership function for gain updating factor is
obtained by translating Fig. 4 along the horizontal axis by an amount +1 and mapping it on [0 1] using the
relation y = 0.5(x+1) and it is shown in Fig. 5. The values of actual inputs e and e are mapped on to [-1 1] by
the input scaling factors. The fuzzy logic controller output is mapped to actual output by output scaling factor
.The actual output of adaptive FLC is obtained by using effective scaling factor GU.
Fig. 2 Membership function plot for Error
+
++
3. Adaptive fuzzy pid regulator for the speed control of brushless dc motor
DOI: 10.9790/1676-10121017 www.iosrjournals.org 12 | Page
Fig.3 Membership function plot for change in Error
Fig.4 Membership function of control output
The relationship between scaling factors and input and output variables of adaptive FLC are given by
Eqns. (3),(4) and (5).
E=GE*e (3)
CE=GCE*e (4)
U=(GU) u (5)
where GE, GCE and GU are scaling factors at the input and output of FLC . The term ‘u’ represents the
fuzzy controller output and ‘U’ denotes scaled output of fuzzy logic controller. The fuzzy PD controller uses
rules of the form:
RPID: If e is PB(Positive Big) and e is PS (Positive Small) then u is PB (Positive Big).
The gain updating factor () are calculated using fuzzy rules of the form:
R : If e is NB (Negative Big) and e is NB (Negative Big )then is VB(Very Big).
Some of the important considerations that have been taken into account for the rule formation is to
minimize the effects of delayed control action due to process dead time or measuring lag. The rules have been
realized of the form : If e is PS and e is PB then is VB.(The process output is not only far away from the set
point but also is moving farther away from it). 2) Using the rule ‘If e is ZE and e is PM then is B’ overshoot
can be reduced. 3) When the processes are subjected to load disturbances in order to improve the control
performance, the gain should be sufficiently large during the steady state condition.
Fig. 5 Membership function for tuning factor' alpha'
4. Adaptive fuzzy pid regulator for the speed control of brushless dc motor
DOI: 10.9790/1676-10121017 www.iosrjournals.org 13 | Page
The fuzzy rules for computation of control input ‘u’ are shown in Table 1 and the fuzzy rules for computation
of are shown in Table 2.
Table1 Fuzzy rules for computation of control input’u’
E/E NB NM NS ZE PS PM PB
NB NB NB NB NB NM NS ZE
NM NB NM NM NM NS ZE PS
NS NB NM NS NS ZE PS PS
ZE NM NM NS ZE PS PM PM
PS NS NS ZE PS PS PM PS
PM NS ZE PS PM PM PM PS
PB ZE PS PM PS PS PS PS
Table 2 Fuzzy rules for computation of
E/e NB NM NS ZE PS PM PB
NB VB VB VB S VS VS ZE
NM VB VB MB SB S S S
NS VB B B MB VS MB SB
ZE B B VB ZE VB B B
PS SB MB VS MB B B VB
PM S S S SB MB VB VB
PB ZE VS VS S VB VB VB
To make controller produce lower overshoot and settling time the controller gain is set at a small value
when error is big. If e is NB and e is PS then is VS . In order to prevent excessive oscillation, the rule for
is selected as ‘if e is PS and e is NS then is ‘S’. This type of gain variation will also result in the convergence
rate of process to the set point. To improve control performance under load disturbance gain should be
sufficiently large around steady state condition. Immediately after a large load disturbance, e may be small but
e will be sufficiently large and in this case is needed to be large to increase the gain. Fig.6 shows the
simulation diagram of BLDC motor control system and Fig.7 demonstrates the fuzzy PIDregulator simulation .
Fig.6 Simulation diagram of BLDC motor system
5. Adaptive fuzzy pid regulator for the speed control of brushless dc motor
DOI: 10.9790/1676-10121017 www.iosrjournals.org 14 | Page
Fig.7 Simulation diagram of Fuzzy regulator
IV. Simulation Results
Simulation resuls includes variations of different parameters of BLDC motor like electromagnetic
torque,rotor speed,line-line motor terminal voltages w.r.t time. Fig.8(a) and Fig.8(b) shows dynamic responses
of the rotor speed using conventional PID Controller and Adaptive fuzzy PID Controller. and Fig.9(a) and
Fig.9(b) shows dynamic responses of the electromanetic torque developed using fuzzzy PID controller and
conventional PID controller.From the torque response curve it can be observed that for the system with fuzzy
PID Controller torque ripple is minimized under variable load conditione.
Fig.8 (a) Rotor speed response using conventionalPID Controller.
Fig.8 (b) Rotor speed response using adaptive fuzzy controller
6. Adaptive fuzzy pid regulator for the speed control of brushless dc motor
DOI: 10.9790/1676-10121017 www.iosrjournals.org 15 | Page
With adaptive fuzzy PID controller percentage overshoot is found to be zero compared to the response
of conventional PID Controller.The produced line-to-line voltage Vab,Vbc and Vca according to the
conduction modes is demonstrated in Fig.10, 11and 12 respectively.
Fig.9 (a) Torque response curve with fuzzy PID Controller
Fig,9(b)Torque response curve with conventional PID Controller
Fig.10 line-line voltage Vab
7. Adaptive fuzzy pid regulator for the speed control of brushless dc motor
DOI: 10.9790/1676-10121017 www.iosrjournals.org 16 | Page
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
-500
0
500
Time
line-linevoltageVbc
Fig.11 line-line voltage Vbc
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
-500
0
500
Time
line-linevoltageVac
Fig.12 line-line voltage VAC
V. Conclusions
A performance comparison of PID Controller and Adaptive fuzzy Logic controller has been carried
out by several simulations. The results have shown that Adaptive fuzzy logic regulator is better than
conventional PID controller under variable operating conditions such as sudden variation in load conditions.
The performance of AFLC is excellent for reducing torque ripples. The conclusion is that Adaptive fuzzy
regulator is found to be more robust, stable flexible and insensitive to parameter variations as compared with
conventional PID Controllers.
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