1. The document presents an intelligent speed control system for an induction motor drive based on fuzzy logic. It replaces the conventional PI controller in the outer speed loop of an indirect vector control system.
2. The fuzzy logic controller uses speed error and change in speed error as inputs and outputs a change in command current. It is composed of fuzzification, fuzzy rules, inference engine, and defuzzification.
3. Simulation results using MATLAB/Simulink show that the proposed fuzzy logic controller provides better performance than a conventional PI controller for different operating conditions like load changes and reference speed changes.
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
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
BIDIRECTIONAL SPEED CONTROL OF DC MOTOR USING 8051 MICROCONTROLLERShanmukha S. Potti
1. This project deals with bidirectional speed control of DC motor using 8051 micro-controller.
2. Design of H bridge dc-dc converter is an IGBT based bridge circuit.
3. The control circuit consists of the 8051 microcontroller which is programmed to generate pulses to turn on IGBTs per required sequence.
4. The H bridge dc-dc converter is implemented with hardware setup and software program in the 8051 –C code.
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
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.
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.
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.
Control of a DC motor using BS2 on a Professional Development Board supplied by Parallax Inc.is attempted. All the control modes like P, PD and PID are carried. The Pulse Width Modulated signal is used to drive the motor at desired speeds.
This is a part of the Mechatronics course offered by Mechanical and Aerospace Engg. Dept. in Spring 2010.
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 DC Motor using PID Controller for Industrial Applicationijtsrd
This paper is to design PID controller to supervise and control the speed response of the DC motor and MATLAB program is used for industrial application . PID controllers are widely used in a industrial plants because of their simplicity and robustness. The results obtained from simulation are approximately similar to that obtained by practical. Also the dynamic behavior is studied. Manoj Kumar Ranwa | Ameen Uddin Ahmad "Speed Control of DC Motor using PID Controller for Industrial Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26599.pdfPaper URL: https://www.ijtsrd.com/engineering/electrical-engineering/26599/speed-control-of-dc-motor-using-pid-controller-for-industrial-application/manoj-kumar-ranwa
BIDIRECTIONAL SPEED CONTROL OF DC MOTOR USING 8051 MICROCONTROLLERShanmukha S. Potti
1. This project deals with bidirectional speed control of DC motor using 8051 micro-controller.
2. Design of H bridge dc-dc converter is an IGBT based bridge circuit.
3. The control circuit consists of the 8051 microcontroller which is programmed to generate pulses to turn on IGBTs per required sequence.
4. The H bridge dc-dc converter is implemented with hardware setup and software program in the 8051 –C code.
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
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.
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.
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.
Control of a DC motor using BS2 on a Professional Development Board supplied by Parallax Inc.is attempted. All the control modes like P, PD and PID are carried. The Pulse Width Modulated signal is used to drive the motor at desired speeds.
This is a part of the Mechatronics course offered by Mechanical and Aerospace Engg. Dept. in Spring 2010.
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 DC Motor using PID Controller for Industrial Applicationijtsrd
This paper is to design PID controller to supervise and control the speed response of the DC motor and MATLAB program is used for industrial application . PID controllers are widely used in a industrial plants because of their simplicity and robustness. The results obtained from simulation are approximately similar to that obtained by practical. Also the dynamic behavior is studied. Manoj Kumar Ranwa | Ameen Uddin Ahmad "Speed Control of DC Motor using PID Controller for Industrial Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26599.pdfPaper URL: https://www.ijtsrd.com/engineering/electrical-engineering/26599/speed-control-of-dc-motor-using-pid-controller-for-industrial-application/manoj-kumar-ranwa
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
Powerpoint Search Engine has collection of slides related to specific topics. Write the required keyword in the search box and it fetches you the related results.
This presentation was presented to Dr. Chongru Liu in North China Electric Power University,Beijing,China by Mr. Aazim Rasool. This presentation will help to understand the control of HVDC system. Animations are not working like ppt. so I apologize on this.
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
Fuzzy-Logic-Controller-Based Fault Isolation in PWM VSI for Vector Controlled...iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
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 paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).
In this paper, the design of a speed control scheme based on a total sliding mode control for Indirect Field Oriented of a three phase induction motor (IM) is proposed. Firstly, the indirect field oriented control is derived. Then, sliding mode control design is investigated to achieve a speed tracking objective under different load torque disturbance. Finally a dSPACE DS1104 R&D board is used to implement the proposed scheme. The experimental results released on 0.25 kW slip-ring IM show a high dynamic performance, fast transient response without overshot as well as a good load disturbances rejection response.
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.
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.
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.
Induction motor harmonic reduction using space vector modulation algorithmjournalBEEI
The vector control was proposed as an alternative to the scalar control for AC machines control. Vector control provide high operation performance in steady state and transient operation. However, the variable switching frequency of vector control causes high flux and torque ripples which lead to an acoustical noise and degrade the performance of the control scheme. The insertion of the space vector modulation was a very useful solution to reduce the high ripples level inspite of its complexity. Numerical simulation results obtained in MATLAB/Simulink show the good dynamic performance of the proposed vector control technique and the effectiveness of the proposed sensorless strategy in the presence of the sudden load torque basing on the integral backstepping approach capabilities on instant perturbation rejection.
Keywords
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
Fuzzy logic speed control of three phase
1. World Academy of Science, Engineering and Technology 60 2011
Fuzzy Logic Speed Control of Three Phase
Induction Motor Drive
P.Tripura and Y.Srinivasa Kishore Babu
Abstract—This paper presents an intelligent speed control and circuit parameters, the plant parameter variation effect can
system based on fuzzy logic for a voltage source PWM inverter-fed be studied. Valuable time is thus saved in the development and
indirect vector controlled induction motor drive. Traditional indirect design of the product, and the failure of components of poorly
vector control system of induction motor introduces conventional PI designed systems can be avoided. The simulation program
regulator in outer speed loop; it is proved that the low precision of the also helps to generate real time controller software codes for
speed regulator debases the performance of the whole system. To
downloading to a microprocessor or digital signal processor.
overcome this problem, replacement of PI controller by an intelligent
controller based on fuzzy set theory is proposed. The performance of Many circuit simulators like PSPICE, EMTP, MATLAB/
the intelligent controller has been investigated through digital SIMULINK incorporated these features. The advantages of
simulation using MATLAB-SIMULINK package for different SIMULINK over the other circuit simulator are the ease in
operating conditions such as sudden change in reference speed and modeling the transients of electrical machines and drives and
load torque. The simulation results demonstrate that the performance to include controls in the simulation. To solve the objective of
of the proposed controller is better than that of the conventional PI this paper MATLAB/ SIMULINK software is used. The
controller. superior control performance of the proposed controller is
demonstrated at SIMULINK platform using the fuzzy logic
Keywords—Fuzzy Logic, Intelligent controllers, Conventional PI tool box [5] for different operating conditions.
controller, Induction motor drives, indirect vector control, Speed The complete paper is organized as follows: Section II
control
describes the indirect vector control system. The design and
description of intelligent controller is provided in section III.
I. INTRODUCTION The simulation results, comparison and discussion are
F OR electrical drives good dynamic performance is
mandatory so as to respond to the changes in command
speed and torques. These requirements of AC drives can
presented in Section IV. Section V concludes the work.
II. INDIRECT VECTOR CONTROL SYSTEM
be fulfilled by the vector control system. With the advent of
For the high performance drives, the indirect method of
the vector control method, an induction motor has been
vector control is preferred choice [1], [2]. The indirect vector
controlled like a separately excited DC motor for high
control method is essentially same as the direct vector control,
performance applications. This method enables the control of
field and torque of induction motor independently except that the rotor angle θe is generated in an indirect
(decoupling) by manipulating corresponding field oriented manner (estimation) using the measured speed ωr and the slip
quantities [1], [2]. speed ωsl . To implement the indirect vector control strategy, it
The traditional indirect vector control system uses
is necessary to take the following dynamic equations into
conventional PI controller in the outer speed loop because of
consideration.
the simplicity and stability. However, unexpected change in
load conditions or environmental factors would produce θ e = ∫ ω e dt = ∫ (ω r + ω sl )dt = θ r + θ sl (1 )
overshoot, oscillation of motor speed, oscillation of the torque,
long settling time and thus causes deterioration of drive For decoupling control, the stator flux component of current
performance. To overcome this, an intelligent controller based ids should be aligned on the d e axis, and the torque component
on Fuzzy Logic can be used in the place of PI regulator [4].
The fuzzy logic has certain advantages compared to classical of current iqs should be on q e axis, that leads to ψ qr = 0 and
controllers such as simplicity of control, low cost, and the ψ dr = ψ r then:
possibility to design without knowing the exact mathematical
Lr dψ r
model of plant [3]. +ψ r = Lm ids ( 2)
In this paper application of fuzzy logic to the intelligent Rr dt
speed control of indirect vector controlled induction motor As well, the slip frequency can be calculated as:
drive is investigated. The analysis, design and simulation of Lm Rr R iqs
controller have been carried out based on the fuzzy set theory. ωsl = iqs = r ( 3)
ψ r Lr Lr ids
When a new control strategy of a converter or a drive
system is formulated, it is often convenient to study the system It is found that the ideal decoupling can be achieved if the
performance by simulation before building the breadboard or above slip angular speed command is used for making field-
prototype. The simulation not only validates the systems dψ r
orientation. The constant rotor flux ψ r and = 0 can be
operation, but also permits optimization of the systems dt
performance by iteration of its parameters. Besides the control substituted in equation (2), so that the rotor flux sets as
P.Tripura is with the Vignan’s Nirula Institute of Science & Technology ψ r = Lm ids ( 4)
for Women, Guntur, A.P., INDIA ( e-mail: tripura.pidikiti@gmail.com).
Y.Srinivasa Kishore Babu is with Vignan University, Vadlamudi, Guntur,
A.P., India (e-mail: yskbabu@gmail.com).
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2. World Academy of Science, Engineering and Technology 60 2011
The Simulink model for such an indirect vector control system logic based controller for IM drives has been proposed by
is shown in the Fig. 3. This control technique operates the Minh Ta-Cao et.al [16]. The performance of the proposed
induction motor as separately excited DC motor so as to system is compared with the conventional vector control on
achieve high dynamic performance [1], [2]. the basis of Integral of time by Absolute Time Error (IATE).
The Simulink implementation of current regulated VSI-fed
III. DESIGN AND DESCRIPTION OF INTELLIGENT CONTROLLER IM is proposed by Norman Mariun et.al [17] and Vinod
Since the implementation of off-line tuning of PI controller Kumar et.al [18]. They proposed a fuzzy logic controller in
is difficult in dealing with continuous parametric variation in place of PI controller in the vector control system. However,
the induction motor as well as the non-linearity present in the the power system block set used by them makes use of S-
entire system, it becomes of interest to go for intelligent functions and it is not as easy to work with as the rest of the
controller. It is known that the stator and rotor resistances of Simulink blocks.
induction motor may change with the temperature up to 50% The work presented in [12]-[18] uses a fuzzy logic
and motor inductance varies with the magnetic operating controller to set the torque component of reference current
point. Furthermore, the load torque may change due to based on speed error and change of speed error. The inverter is
mechanical disturbances. then switched to follow the reference current within hysteresis
The problem can be solved by several adaptive control band. However, the constant hysteresis band of the current
techniques such as model reference adaptive control, sliding- regulated PWM inverter of the fuzzy logic based indirect
mode control, variable structure control, and self-tuning PI vector control system possesses problem in achieving superior
controllers, etc. The theory and survey on model reference dynamic performance, even the drive control system includes
adaptive system has been reported by H. Sugimoto et.al [6]. the efficient fuzzy logic controller. This paper discusses the
Secondary resistance identification of an IM applied with fuzzy logic speed control for VSI fed indirect vector
MRAS and its characteristics has been presented in their controlled induction motor drives.
study. The improved version of sliding mode control for an IM Fig. 1 shows the block diagram of fuzzy logic based speed
has been proposed by C. Y. Won et.al [7]. The design of control system. Such a fuzzy logic controller consists of four
integral variable structure control system for servo systems basic blocks viz., Fuzzification, Fuzzy Inference Engine,
has been proposed by T. L. Chern et.al [8]. The self tuning Knowledge base and defuzzification.
controllers are described by J. C. Hung [9]. However, in all
these works, exact mathematical model of the system is ωr ( k )
*
eω ( k )
∫
IVC +
mandatory to design the adaptive control algorithm. Thus they Fuzzy PWM
increase the complexity of design and implementation.
When fuzzy logic bases intelligent controller is used instead ωr ( k ) d/dt Controller Inverter +
IM
ceω ( k ) ciqs ( k ) i* k
*
qs ( )
of the PI controller, excellent control performance can be
achieved even in the presence of parameter variation and drive
non-linearity [1], [3].
In addition, the fuzzy logic posses the following Fig. 1 Block diagram of Fuzzy logic speed control system for indirect
advantages: (1) The linguistic, not numerical, variables make vector controlled induction motor drive
the process similar to the human think process. (2) It relates
output to input, without understanding all the variables, A. Input/ Output variables
permitting the design of system more accurate and stable than
The design of the fuzzy logic controller starts with
the conventional control system. (3) Simplicity allows the
assigning the input and output variables. The most significant
solution of previously unsolved problems. (4) Rapid
variables entering the fuzzy logic speed controller has been
prototyping is possible because, a system designer doesn’t
selected as the speed error and its time variation. Two input
have to know everything about the system before starting
work. (5) It has increased robustness. (6) A few rules variables eω ( k ) and ceω ( k ) , are calculated at every
encompass great complexity. sampling instant as:
The vector control of IM with fuzzy PI controller has been
proposed by I. Miki et.al [10] and W. P. Hew et.al [11]. As
eω ( k ) = ωr ( k ) − ωr ( k )
*
( 5)
they reported, the FLC automatically updates the proportional
and integral gains on-line and thus help in achieving fast ceω ( k ) = eω ( k ) − eω ( k − 1) (6)
dynamic response. However, this technique does not fully
where ωr ( k ) is the reference speed, ωr ( k ) is the actual rotor
*
utilize the capabilities of the fuzzy logic. Moreover, the
inherent disadvantages associated with the PI controller cannot speed and eω ( k − 1) is the value of error at previous sampling
be avoided. The fuzzy PI controllers are less useful in time.
industrial applications. The output variable of the fuzzy logic speed controller is the
The performances of the fuzzy logic based indirect vector
control for induction motor drive has been proposed by M. N. variation of command current, ciqs ( k ) which is integrated to
*
Uddin et.al [12], E. Cerruto et.al [13], B. Heber et.al [14], and get the reference command current, iqs ( k ) as shown in the
*
G. C. D. Sousa et.al [15]. The novel speed control for current
regulated VSI-fed IM has been discussed by them. The fuzzy following equation.
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3. World Academy of Science, Engineering and Technology 60 2011
iqs ( k ) = iqs ( k − 1) + ciqs ( k )
* * *
(7) ( )
*
µ ciqs
NL NM NS ZE PS PM PL
1.0
B. Fuzzification
The success of this work, and the like, depends on how
good this stage is conducted. In this stage, the crisp variables
eω ( k ) and ceω ( k ) are converted in to fuzzy variables eω 0.5
and ceω respectively. The membership functions associated
to the control variables have been chosen with triangular
shapes as shown in Fig. 2. 0
The universe of discourse of all the input and output -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
variables are established as (-0.8, 0.8). The suitable scaling
(c)
factors are chosen to brought the input and output variables to Fig. 2 Membership functions for (a) speed error (b) change of speed
this universe of discourse. Each universe of discourse is error (c) Change of command current
divided into seven overlapping fuzzy sets: NL (Negative
Large), NM (Negative Medium), NS (Negative Small), ZE C. Knowledge base and Inference Stage
(Zero), PS (Positive Small), PM (positive Medium), and PL Knowledge base involves defining the rules represented as
(Positive Large). Each fuzzy variable is a member of the IF-THEN statements governing the relationship between input
subsets with a degree of membership µ varying between 0 and output variables in terms of membership functions. In this
(non-member) and 1 (full-member). All the membership stage, the variables eω and ceω are processed by an
functions have asymmetrical shape with more crowding near inference engine that executes 49 rules (7x7) as shown in
the origin (steady state). This permits higher precision at Table I. These rules are established using the knowledge of the
steady state [3]. system behavior and the experience of the control engineers.
Each rule is expressed in the form as in the following
µ ( eω) example: IF ( eω is Negative Large) AND ( ceω is Positive
*
Large) THEN ( ciqs is Zero). Different inference engines can
NL NM NS ZE PS PM PL
1.0 be used to produce the fuzzy set values for the output fuzzy
*
variable ciqs . In this paper, the Max-product inference method
[3] is used.
0.5
TABLE I
FUZZY CONTROL RULES
e NL NM NS ZE PS PM PL
ce
0 NL NL NL NL NL NM NS ZE
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 NM NL NL NL NM NS ZE PS
(a) NS NL NL NM NS ZE PS PM
ZE NL NM NS ZE PS PM PL
PS NM NS ZE PS PM PL PL
µ ( ceω) PM NS ZE PS PM PL PL PL
PL ZE PS PM PL PL PL PL
NL NM NS ZE PS PM PL
1.0
D.Defuzzification
In this stage a crisp value of the output variable ciqs ( k ) is
*
0.5 obtained by using height defuzzufication method, in which the
centroid of each output membership function for each rule is
first evaluated. The final output is then calculated as the
average of the individual centroid, weighted by their heights
0 (degree of membership) as follows:
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
(b)
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4. World Academy of Science, Engineering and Technology 60 2011
Vref
wr* vao
-K- Vref vao ia
-K- 1/s iqs*
wr* PI
[iqs]
wr Integrator
Fuzzy Logic ids* vqs* vao*
Controller vao*
vbo ib
du/dt -K-
vbo
Derivative
vco ic
1/lm vds* vbo* vbo*
PI
[ids]
tl te
vco
we vco* vco*
Demo
lm/(tr) we wr
Command Voltage PWM inverter
Generator induction motor
model
1 wsl
-C- |u|
u
absolute peak
rotor flux Tl
Load Torque
Fig. 3 Indirect vector controlled induction motor block diagram with the Fuzzy Logic Controller
n look-up table. The intelligent controller exhibited better speed
∑ µ ( ciqs )i ( ciqs )i
*
*
tracking compared to PI controller.
ciqs ( k ) =
* i =1
n
(8)
i =1
( )
∑ µ ciqs i
*
400
300
Speed, rad/sec
The reference value of command current iqs ( k ) that is
* Reference Speed
200
Response with FL Controller
applied to vector control system is computed by the equation
(7). 100 Response with PI Controller
The overall model for fuzzy logic based speed control
system for indirect vector controlled induction motor drive is 0
shown in Fig. 3. The parameters of the motor are given in
-100
appendix. 0 0.2 0.4 0.6 0.8 1.0 1.2
Time, sec
IV. SIMULATION RESULTS AND DISCUSSION
Fig. 4 Speed response comparison at no-load
A series of simulation tests were carried out on indirect
302
vector controlled induction motor drive using both the PI Reference Speed
controller and fuzzy logic based intelligent controller for
301 Response with FL Controller
Speed, rad/sec
various operating conditions. The time response and steady Response with PI Controller
state errors were analyzed and compared. 300
Figures 4 and 5 shows speed response with both the PI and
FL based controller. The FL controller performed better 299
performance with respect to rise time and steady state error.
Figure 6 shows the load disturbance rejection capabilities of 298
0 0.2 0.4 0.6 0.8 1.0 1.2
each controller when using a step load from 0 to 20 N-m at 0.8 Time, sec
seconds. The FL controller at that moment returns quickly to Fig. 5 Enlarged speed response comparison at no-load
command speed, where as the PI controller maintains a steady
state error.
Figure 7 shows the speed tracking performance test, when
sudden change in speed reference is applied in the form of
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5. World Academy of Science, Engineering and Technology 60 2011
IEEE Transactions on Industry Applications, Vol. 38, No. 5, pp. 1219-
400 1225, September/October, 2002.
[5] http://www.mathworks.com/ (The official site for
300 MATLAB&SIMULINK as well as Fuzzy Logic Toolbox).
Speed, rad/sec
Reference Speed [6] H. Sugimoto and S. Tamai, “Secondary resistance identification of an
200 Induction Motor Applied Model reference Adaptive Systems and its
Response with FL Controller
Characteristics”, IEEE Trans. on Ind. Appl., Vol IA-23, No.1, pp.296-
100 Response with PI Controller 303, Mar/Apr, 1987.
[7] C. Y. Won and B. K. Bose, “An induction Motor servo Systems with
Improved Sliding Mode Control”, in Proc. IEEE IECON’92, pp. 60-66.
0
[8] T. L Chern and Y. C. Wu, “Design of Integral Variable Structure
Controller and Applications to Electro Hydraulic Velocity Servo
-100 Systems”, Proc. In Elec. Eng., Vol. 138, no. 5, pp. 439-444, Sept. 1991.
0 0.2 0.4 0.6 0.8 1.0 1.2
Time, sec [9] J. C. Hung, “Practical Industrial Control techniques”, in Proc. IEEE
IECON’94, pp. 7-14.
[10] Miki, N. Nagai, S. Nishigama, and T. Yamada, “Vector control of
Fig. 6 Speed response comparison during sudden load change induction motor with fuzzy PI controller”, IEEE IAS Annu. Meet. Conf.
Rec., pp. 342-346, 1991.
[11] W. P. Hew, M. R. Tamjis, and S. M. Saddique, “Application of Fuzzy
400 Logic in Speed Control of Induction Motor Vector Control”, Proc. Of
Reference Speed
the international conference on Robotics, vision and Parallel Processing
300 for Industrial Automation, pp. 767-772, Ipoh, Malasiya, Nov. 28-
Response with FL Controller
Speed, rad/sec
30,1996
200 Response with PI Controller [12] M. N. Uddin, T. S. Radwan and M. A. Rahman “Performances of Fuzzy-
100 Logic-Based Indirect Vector Control for Induction Motor Drive,” IEEE
Transactions on Industry Applications, Vol. 38, No. 5, pp. 1219-1225,
0 September/October, 2002.
[13] E. Cerruto, A. Consoli, A. Raciti and A. Testa, “ Fuzzy Adaptive Vector
-100 Control of Induction Motor Drives”, IEEE Trans, on Power Electronics,
Vol.12, No. 6, pp. 1028-1039, Nov. 1997.
-200 [14] B. Hebetler, L. Xu, and Y.Tang, “Fuzzy Logic Enhanced Speed Control
0 0.2 0.4 0.6 0.8 1.0 1.2
Time, sec of Indirect Field Oriented Induction Machine Drive”, IEEE Trans. On
Power Electronics, Vol.12, No.5. pp. 772-778, Sept.1997.
[15] G. C.D. Sousa, B.K. Bose and J.G. Cleland, “Fuzzy Logic based On-
Fig. 7 Speed tracking response comparison Line Efficiency Optimization Control of an Indirect Vector Controlled
Induction Motor Drive” , IEEE Trans. On Industrial Electronics, Vol.
42, No. 2 , pp.192-198, April 1995.
[16] Minh Ta-Cao, J. L. Silva Neto and H. Le-Huy, “Fuzzy Logic based
V. CONCLUSION Controller for Induction Motor Drives”, Canadian Conference on
Electrical and Computer Engineering, Volume 2, Issue, 26-29 May 1996
The performance of fuzzy logic based intelligent controller Page(s):631 - 634 vol.2.
for the speed control of indirect vector controlled, PWM [17] Norman Mariun, Samsul bahari Mohd Noor, J. Jasni and O. S.
voltage source inverter fed induction motor drive has been Bennanes, “A Fuzzy Logic based Controller for an Indirect Vector
Controlled Three-Phase Induction Motor”, IEEE Region 10 Conference,
verified and compared with that of conventional PI controller TENCON 2004, Volume D, Issue. 21-24 Nov. 2004 Page(s): 1-4 Vol. 4
performance. The simulation results obtained have confirmed [18] Vinod Kumar, R. R. Joshi, “Hybrid Controller based Intelligent Speed
the very good dynamic performance and robustness of the Control of Induction Motor”, Journal of Theoretical and Applied
fuzzy logic controller during the transient period and during Information Technology, December 2006, Vol. 3 No. 1, pp. 71- 75.
the sudden loads. It is concluded that the proposed intelligent
controller has shown superior performance than that of the
parameter fixed PI controller and earlier proposed system [4].
APPENDIX
3-Phase Induction Motor Parameters
Rotor type: Squirrel cage,
Reference frame: Synchronous
10 hp, 314 rad/sec, 4 Poles, Rs = 0.19 , Rr = 0.39 , Lls =
0.21e-3 H, Llr = 0.6e-3 H, Lm = 4e-3 H, J = 0.0226 Kg-m2.
REFERENCES
[1] Bimal K. Bose, Modern Power Electronics and AC Drives, Third
impression, INDIA: Pearson Education, Inc., 2007.
[2] Blaschke F, "The Principle of Field-Orientation as applied to the New
Transvector Closed-Loop Control System for Rotating-Field Machines,"
Siemens Review, Vol. 34, pp. 217-220, May 1972.
[3] C. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Control – Part
1,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 20, No.
2, pp. 404-418, March/April, 1990.
[4] M. N. Uddin, T. S. Radwan and M. A. Rahman “Performances of
Fuzzy-Logic-Based Indirect Vector Control for Induction Motor Drive,”
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