1. The document describes a hybrid PI-fuzzy controller for speed control of a brushless DC motor.
2. It combines a proportional-integral controller, which performs well near steady-state, with a fuzzy logic controller, which handles transients better by reducing overshoot and oscillations.
3. By switching between the two controllers according to system conditions, the hybrid controller aims to achieve quick response during normal operation while minimizing overshoot during transients, obtaining the benefits of both.
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.
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
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.
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
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.
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
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.
Modelling and Simulation of DC-Motor Electric Drive Control System with Varia...IDES Editor
This work represents a mathematical analysis and
simulation of dc-motor electric drive control system with
variable moment of inertia. A separately-excited dc motor is
used in this control system. A mathematical model for this
motor has been simulated and tested in Matlab/Simulink. A
closed-loop control system for this dc electric drive system is
proposed. The proposed control system is based on the
technical optimum method of design. The controlled variable
of this system is the load angular speed. In this control system
the moment of inertia is considered to be variable. It varies as
a function of time. A speed controller and a current controller
are designed for the suggested model to meet the desired
performance specifications by using the technical optimum
method. These controllers are attached to the control system
and the closed-loop response is observed by simulation and
testing this model. The results show the high-performance of
the designed control system.
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.
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.
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
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.
Modelling and Simulation of DC-Motor Electric Drive Control System with Varia...IDES Editor
This work represents a mathematical analysis and
simulation of dc-motor electric drive control system with
variable moment of inertia. A separately-excited dc motor is
used in this control system. A mathematical model for this
motor has been simulated and tested in Matlab/Simulink. A
closed-loop control system for this dc electric drive system is
proposed. The proposed control system is based on the
technical optimum method of design. The controlled variable
of this system is the load angular speed. In this control system
the moment of inertia is considered to be variable. It varies as
a function of time. A speed controller and a current controller
are designed for the suggested model to meet the desired
performance specifications by using the technical optimum
method. These controllers are attached to the control system
and the closed-loop response is observed by simulation and
testing this model. The results show the high-performance of
the designed control system.
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.
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.
Brushless DC Motor(BLDC)
A BLDC is simply a normal dc motor turned inside out,that means the coil is on the out side and the magnets are inside
The stator consists of several coils which current is led through Creating a magnetic field that makes the rotor turns
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.
Here's the powerpoint of the presentation Nuts&Bold gave to the Museum Computer Network 2013 (#MCN2013).
The first part is all about our inspirations, the second one is the presentation of the process used for Variations, an entirely crowdsourced online exhibit, and the final part is about our current research.
Have fun and if you'd like more info connect yourself to our Facebook page www.facebook.com/nutsandbold.ca or linkedIn or twitter (@nutsbold) or contact us at info@nutsandbold.ca. We will be happy to talk to you some more about our process, the possibilities, etc.
Have fun and let us know what you thinker of the exhibit !
http://commissairesglaneurs.wix.com/variations
nutsandbold.ca
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.
Type 1 versus type 2 fuzzy logic speed controllers for brushless DC motors IJECEIAES
This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.
Speed and Torque Control of Mechanically Coupled Permanent Magnet Direct Curr...IDES Editor
A new controller is designed for speed and torque
control of a Permanent Magnet DC motor based on
measurements of speed and current. This research work
focuses on investigating the effects of control of the speed and
torque of two brushless dc motors that are mechanically
coupled. Two controller design methods: the Root Locus
method and Bode Plot method as well as two controllers:
Proportional-Integral-Derivative (PID) and Proportional-
Integral (PI) are used to obtain the control objectives of speed
control and torque control. The simulation is performed using
MATLAB/SIMULINK software. The effects of varying the
controller gains on the system performance is studied and
quantified. The simulation results show that the speed control
objectives of the motor are satisfied even in the case of torque
disturbance from the other motor.
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.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
Hybrid PI-Fuzzy Controller for Brushless DC motor speed control
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 8, Issue 6 (Nov. - Dec. 2013), PP 33-43
www.iosrjournals.org
www.iosrjournals.org 33 | Page
Hybrid PI-Fuzzy Controller for Brushless DC motor speed
control
Dhanya K Panicker1
, Ms. Remya Mol2
1,2
(Electrical and Electronics Engineering,Govt.Engineering College Idukki/M.G.University, India)
Abstract : This paper aims at the design and simulation of hybrid PI-fuzzy control system for the speed
control of a brushless dc motor. The performance of the fuzzy logic controller (FLC) is better under transient
conditions, while that of the proportional plus integral (PI) controller is superior near the steady-state
condition. The combined advantages of these two controllers can be obtained with hybrid PI -fuzzy speed
controller. Both the design of the fuzzy controller and its integration with the proportional-integral (PI)
controller is to be done. The principle of the proposed control system is to use a PI controller, which performs
satisfactorily in most cases, while keeping in the background, a fuzzy controller, which is ready to take over
the PI controller when severe perturbations occur. By combining the two controllers, one can get the quick
response of the PI controller while eliminating the overshoot possibly associated with it.
Keywords: PI, Fuzzy logic, Hybrid Controller, BLDC Motor, Speed Control.
I. INTRODUCTION
In recent years, brushless dc (BLDC) machines have gained widespread use in electric drives. These
machines are ideal for use in clean, explosive environments such as aeronautics, robotics, electric vehicles, food
and chemical industries and dynamic actuation. Using these machines in high-performance drives requires
advance and robust control methods. Conventional control techniques require accurate mathematical models
describing the dynamics of the system under study. These techniques result in tracking error when the load
varies fast and overshoot during transients. In lieu of provisions for robust control design, they also lack
consistent performance when changes occur in the system. If advance control strategies are used instead, the
system will perform more accurately or robustly. It is therefore, desired to develop a controller that has the
ability to adjust its own parameters and even structure online, according to the environment in which it works to
yield satisfactory control performance. An interesting alternative that could be investigated is the use of fuzzy
logic control (FLC) methods. In the last decade, FLC has attracted considerable attention as a tool for a novel
control approach because of the variety of advantages that it offers over the classical control techniques. Unlike
other conventional control schemes, FLC is a model-free controller. It does not require an exact mathematical
model of the controlled system and therefore, is less sensitive to system parameter changes. In addition, rapidity
and robustness are the most profound and interesting properties in comparison to the traditional control
methods.
II. MATHEMATICAL MODEL OF BLDC MOTOR
BLDC motor can be modeled in the 3-phase abc variables which consist of two parts. One is an
electrical part which calculates electromagnetic torque and current of the motor. The other is a mechanical part,
which generates revolution of the motor. The electrical part of BLDC motor can be represented in matrix form
as follows:
= + d/dt + (1)
Where Van , Vbn and Vcn are the phase winding voltages, R is the resistance per phase of the stator
winding, while ia , ib and ic are the phase currents. The back-EMF waveforms ea, eb, ec are functions of angular
velocity of the rotor shaft, so
E = Ke ώm , where Ke is the back-emf constant.
If there is no change in rotor reluctance with angle because of a non-salient rotor, and assuming three
symmetric phases, the following are obtained.
La = Lb = Lc = L (2)
Lab = Lba = Lac = Lca = Lbc = Lcb = M (3)
cn
bn
an
V
V
V
R
R
R
00
00
00
c
b
a
i
i
i
ccbca
cbbba
LLL
LLL
LLL cabaa
c
b
a
i
i
i
c
b
a
e
e
e
2. Hybrid PI-Fuzzy Controller for Brushless DC motor speed control
www.iosrjournals.org 34 | Page
Substituting equations (2) and (3) in (1) gives the BLDC model as:
= + d/dt + (4)
From equation (4), phase voltage for phase A, Van can be derived as:
Van = Rsia + L dia/dt + M d(ib + ic) /dt + ea
Assume stator currents to be balanced.
Van = Rs ia + ( L – M) dia/dt + ea
This is also same for phase b and c.
= +Ld/dt + (5)
Neutral voltages are referred to the zero reference potential at midpoint of dc-link (0). So phase voltage
can be expressed as:
Van = Vao – Vno
Vbn = Vbo – Vno
Vcn = Vco – Vn0
where Vao, Vbo, Vco and Vno are the output phase voltages from the inverter and the potential of the star
point referred to the neutral respectively. In order to avoid unbalance in applied voltages, a balanced star
connected three phase winding is considered.
The developed electromagnetic torque can be expressed as:
Te=Jdω/dt+Bω+Tl
where J is moment of inertia in kg-m2
, B is frictional coefficient in N-ms/rad , Tl is the load torque in
Nm. But the electromagnetic torque for this 3-phase BLDC motor is dependent on the current, speed and back-
EMF waveforms, so the instantaneous electromagnetic torque can be represented as:
Te= (eaia+ebib+ecic)/ω.
The derivative of electrical rotor position r is expressed as:
where P is the number of poles, ωr is the rotor speed in mechanical rad/sec and θ r is the electrical rotor
position in electrical radian.
III. SPEED CONTROLLER
Many drive systems today employ a conventional controller such as a PI-type controller. This method
works well, but only under a specific set of known system parameters and load conditions. However, deviations
of the system parameters or load conditions from the known values cause the performance of the closed-loop
system to deteriorate, resulting in larger overshoot, larger rise time, longer settling times and, possibly, an
unstable system. It should be noted that the system parameters such as the system inertia and damping ratio
might vary over a wide range due to changes in load conditions. Generally, a PI speed controller could be tuned
to a certain degree in order to obtain a desired performance under a specific set of operating conditions. Less
than ideal performance is then observed when these operating conditions vary. Thus, there is a need for other
types of controllers, which can account for nonlinearities and are somewhat adaptable to varying conditions in
real time. Other methods are now being employed, such as fuzzy logic, in order to achieve a desired
performance level.
cn
bn
an
V
V
V
R00
0R0
00R
c
b
a
i
i
i
LMM
MLM
MML
c
b
a
i
i
i
c
b
a
e
e
e
0cibiai
cn
bn
an
V
V
V
R
R
R
00
00
00
c
b
a
i
i
i
ML
ML
ML
00
00
00
c
b
a
i
i
i
c
b
a
e
e
e
3. Hybrid PI-Fuzzy Controller for Brushless DC motor speed control
www.iosrjournals.org 35 | Page
Fig. 1 Closed loop speed control of BLDC motor
The model of speed controller has been realized using the Simulink toolbox of the MATLAB software.
The main function of speed controller block is to provide a reference torque which in turn is converted to
reference current and is fed to current reference generator. The output of the speed controller is limited to a
proper value in accordance with the motor rating to generate the reference torque. The speed controllers realized
in this project are proportional integral (PI) controller, fuzzy logic (FLC) controller and hybrid PI-Fuzzy
controller.
A. PI Controller
The proportional plus integral (PI) controller is widely used for industrial applications. The input to the
PI controller is the speed error (E), while the output of the PI controller is used as the input of reference current
block.
B. Fuzzy Logic Controller
Fuzzy logic control (FLC) is a rule based controller. It is a control algorithm based on a
linguistic control strategy which tries to account the human’s knowledge about how to control a system without
requiring a mathematical model. The approach of the basic structure of the fuzzy logic controller system is
illustrated in Fig.2 .
Fig. 2 Basic structure of Fuzzy Logic controller
Fuzzy logic uses linguistic variables instead of numerical variables. The process of converting a
numerical variable (real number or crisp variables) into a linguistic variable (fuzzy number) is called
Fuzzification. Here the inputs for Fuzzy Logic controller are the speed error (E) and change in speed error (CE).
Speed error is calculated with comparison between reference speed and the actual speed. The fuzzy logic
controller is used to produce an adaptive control so that the motor speed can accurately track the reference
speed. The reverse of Fuzzification is called Defuzzification. The use of Fuzzy Logic Controller (FLC) produces
required output in a linguistic variable (fuzzy number). According to real world requirements, the linguistic
variables have to be transformed to crisp output.
The membership function is a graphical representation of the magnitude of participation of each input.
There are different membership functions associated with each input and output response. Here the trapezoidal
membership functions are used for input and output variables. The number of membership functions determines
the quality of control which can be achieved using fuzzy controller. As the number of membership function
increases, the quality of control improves. As the number of linguistic variables increases, the computational
time and required memory increases. Therefore, a compromise between the quality of control and computational
time is needed to choose the number of linguistic variables. The most common shape of membership functions
is triangular, although trapezoidal and bell curves are also used, but the shape is generally less important than
the number of curves and their placement.
4. Hybrid PI-Fuzzy Controller for Brushless DC motor speed control
www.iosrjournals.org 36 | Page
The processing stage is based on a collection of logic rules in the form of IF-THEN statements, where
the IF part is called the "antecedent" and the THEN part is called the "consequent". The knowledge base
comprises knowledge of the application domain and the attendant control goals. It consists of a data “base” and
a linguistic (fuzzy) control rule base. The data base provides necessary definitions, which are used to define
linguistic control rules and fuzzy data manipulation in an FLC. The rule base characterizes the control goals and
control policy of the domain experts by means of a set of linguistic control rules. Decision making logic is the
kernel of an FLC.
The most important things in fuzzy logic control system designs are the process design of membership
functions for input, outputs and the process design of fuzzy if-then rule knowledge base. Fig 3 shows the
membership function of speed error (E), change in speed error (CE) and figue.4 shows the membership function
of output variable. In practice, one or two types of membership functions are
Table. 1 Rule base of Fuzzy logic controller
Enough to solve most of the problems. The next step is to define the control rules. There are no specific
methods to design the fuzzy logic rules. However, the results from PI controller give an opportunity and
guidance for rule justification. Therefore after thorough series of analysis, the total 49 rules have been justified
as shown in Table 1.
The membership function is divided into seven sets: NB: Negative Big, NM: Negative Medium, NS:
Negative Small, Z: Zero, PS: Positive Small, PM: Positive Medium, PB: Positive Big.
Fig.3 Membership function plots, error and change in error
Fig 4. Membership function plots, output variable
C. Hybrid PI-Fuzzy Controller
The objective of the hybrid controller is to utilize the best attributes of the PI and
fuzzy logic controllers to provide a controller which will produce better response than either the PI or the fuzzy
controller. There are two major differences between the tracking ability of the conventional PI controller and the
fuzzy logic controller. Both the PI and fuzzy controller produce reasonably good tracking for steady-state or
slowly varying operating conditions. However, when there is a step change in any of the operating conditions,
such as may occur in the set point or load, the PI controller tends to exhibit some overshoot or oscillations. The
fuzzy controller reduces both the overshoot and extent of oscillations under the same operating conditions.
Although the fuzzy controller has a slower response by itself, it reduces both the overshoot and extent of
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oscillations under the same operating conditions. The desire is that, by combining the two controllers, one can
get the quick response of the PI controller while eliminating the overshoot possibly associated with it.
Switching Control Strategy
The switching between the two controllers needs a reliable basis for determining which controller
would be more effective. The answer could be derived by looking at the advantages of each controller. Both
controllers yield good responses to steady-state or slowly changing conditions. To take advantage of the rapid
response of the PI controller, one needs to keep the system responding under the PI controller for a majority of
the time, and use the fuzzy controller only when the system behavior is oscillatory or tends to overshoot. Thus,
after designing the best stand-alone PI and fuzzy controllers, one needs to develop a mechanism for switching
from the PI to the fuzzy controllers, based on the following two conditions:
1) Switch when oscillations are detected;
2) Switch when overshoot is detected.
The switching strategy is then simply based on the following conditions:
IF the system has an oscillatory behavior
THEN fuzzy controller is activated,
Otherwise PI controller is operated.
IF the system has an overshoot
THEN fuzzy controller is activated,
Otherwise PI controller is operated.
The system under study is considered as having an overshoot when the error is zero and the rate of
change in error is any other value than zero. The system is considered oscillatory when the sum of the absolute
values of the error taken over time does not equal the absolute values of the sum of the error over the same
period of time. Since the system is expected to overshoot during oscillatory behavior, the only switching
criterion that needs to be considered is overshoot. However, in practice, it is more convenient to directly
implement the control signal according to the control actions delivered by the controller. Consequently, the
fuzzy controller can be designed so that normal behavior (no oscillations or overshoot) results in a null fuzzy
action. Accordingly, the switching between the two controllers reduces to using PI if the fuzzy has null value;
otherwise, the fuzzy output is used. In particular, the fuzzy controller can be designed so that a normal behavior
Fig.5 Structure of switching strategy results in a null fuzzy action.
IV. Simulation And Results
A. Body Block Of Bldc Motor
Fig. 6 shows the simulation model of the electrical block of BLDC motor
Fig. 6 Electrical block of phase a
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Fig.7 and Fig.8 shows the simulation models for building mechanical block of BLDC motor
Fig.7 Generation of electromagnetic torque
Fig.8 Simulink model for the generation of rotor speed
C. Commutation Logic Block
The reliable commutation of inverter for BLDC motor control system is controlled by the rotor position
signal. The function of this block is to decide the switching order of six power tubes according to position of the
magnetic poles. The position signal of magnetic poles acts as inputs, and the switching logic signals of six
power tubes act as outputs, as shown in Fig.9 and 10
D. Hall Sensor Signal Generation
Fig.9 Generation of Hall sensor signals
D. Gate Signal Generation
Fig.10 Generation of gate signals
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E. Voltage Source Inverter
The voltage inverter block uses three-phase full-bridge inverter. The model of six power tubes can be
built by using switching devices MOSFET in Simulink/SimPowerSystems Toolbox, as shown in Fig.11
Fig.11 Voltage source inverter
F. Open Loop Bldc
Fig.12 Simulink model of open loop BLDC motor
Fig.13 shows the generated hall signals
Fig.13 Hall sensor signals
Fig.14 shows the voltage source inverter output.
Fig.14 Voltage source inverter output
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Fig.15 Back emf waveforms
G. Open Loop Speed Response
Fig.16 shows the open loop speed response of BLDC motor.
Fig.16 Open loop speed response of BLDC motor
H. . Current Controller
H. A. Current Reference Block
The action of the current reference block is to produce three-phase reference currents, iar, ibr, icr
depending on the signal of current amplitude, Is, and the position signal, Then the reference currents are directly
given to the current hysteresis controller block. Table 2 shows corresponding relation between the rotor position
and the three-phase reference current
Table. 2 Relationship between reference current and rotor position
Fig.17 Reference current generator
H.B. Hysteresis Current Controller Block
Current hysteresis controller block is to achieve hysteresis current control, in which input signals are
three phase reference currents and practical currents, and output signals will act as the control signals of
inverter. When the practical current is greater than the reference current and the error is greater than the ring
width of hysteresis comparator, the corresponding phase will be conducted forward and turn off reversely.
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Conversely, it will be conducted reversely and turn off forward. When selecting proper hysteresis ring width, the
practical current will track the reference current continuously. By this means the current closed-loop control can
be achieved.
Fig.18 Hysteresis current controller
Closed Loop Control Of Bldc Motor Using Pi-Controller
Fig.19 Simulink block of closed loop speed control using PI controller
Fig.20 Speed response using PI controller in closed loop
Fig.21 Electromagnetic Torque response using PI controller in closed loop
Fuzzy Logic Controller
Here the inputs for Fuzzy Logic controller are the speed error (E) and change of speed error (CE).
Speed error is calculated with comparison between reference speed and the actual speed.
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Fig.22 Simulink model of Fuzzy logic controller
Fig.23 shows the speed response of BLDC motor while using fuzzy logic controller as the speed controller.
Fig.23 Speed response using fuzzy logic controller
Hybrid Pi- Fuzzy Controller
The objective of the hybrid controller is to utilize best attributes of the PI controller and fuzzy
controller to provide a controller which will produce better response than either PI or fuzzy controller. The
superiority of both fuzzy and PI controller are integrated together by using a switch as shown in Fig.24. The
speed error and simulation time control the switch such that it will be switched to fuzzy controller when the
speed error is large and vice versa to PI controller when the speed error is small with appropriate time.
Fig.24 Simulink model of Hybrid PI- Fuzzy controller
Fig.25 shows the speed response of BLDC motor while using Hybrid PI – fuzzy controller as the speed
controller
Fig.25 Speed response using hybrid PI-fuzzy controller
Fig.26 Electromagnetic Torque response using hybrid PI-fuzzy controller
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V. COMPARISON BETWEEN SPEED CONTROLLERS
When a step change at 0.5 was applied the PI controller failed to track the reference speed of 1000rpm
which was well tracked by the fuzzy controller. During steady state conditions, the settling time of fuzzy
controller was found to be more when compared to PI controller. In hybrid PI Fuzzy controller this is
compensated by the combined action of PI and Fuzzy controller.
VI. CONCLUSION
This paper presented the design of a hybrid PI-fuzzy control system for the speed control of a brushless
dc motor The performance of the fuzzy logic controller is better under transient conditions, while that of the
proportional plus integral controller is superior near the steady state condition. The combined advantages of
these two controllers can be obtained with hybrid PI-fuzzy speed controller. Mathematical model of the BLDC
motor is studied. Based on this, the modeling and simulation of the proposed control system is done. The
performance of the system using PI controller, fuzzy logic controller and hybrid PI- fuzzy controller are
compared. It is shown through extensive simulation that the performance of the Hybrid PI- Fuzzy controller is
better than using PI controller and fuzzy controller alone for the speed control of BLDC motor.
REFERENCES
[1] Ahmed R., Daniel R. and David M.D. “Experimental Verification of a Hybrid Fuzzy Control Strategy for a High-Performance
Brushless DC Drive system”. IEEE Transactions on Industry Applications, Vol.37, No.2, March-April 2001, pp.503-512
[2] Tan C.S., Baharuddin I. “Study of Fuzzy and PI controller for permanent magnet brushless dc motor drive”. Proceedings of
International Power Engineering and Optimization Conference, June 23-24, 2010, pp.517- 521
[3] Sayed H.S., Khouly F.M., “Fuzzy logic based control of a permanent magnet brushless dc motor drive” AIAA, No.78-1291, 1978.
[4] Dumitru D., Chemin B.,“A PI-Fuzzy Controller Designated For Industrial Motor Control Application”. AIAA, 1990, pp.949-954
[5] Arnulfo R.M., Raul G.R., “ PI Fuzzy Gain- Scheduling Speed Control at Startup of a Gas-Turbine Power Plant” IEEE Transactions on
energy conversion, vol.26, NO.1, March 2011, pp.467-473