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.
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.
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
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.
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
Performance Analysis of Direct Torque Controlled BLDC motor using Fuzzy LogicIAES-IJPEDS
The Brushless DC motor (BLDC) control is used in many of the applications
as it is small in size and with low power which can drive in high speed and
lighter compared to other motors.The electric vehicles are built with BLDC
motors and also in ships, aerospace etc., The control of BLDC motors is done
with sensors like hall effect sensor for sensing the positions. The speed
control can be done with normal PI and PID controllers. Direct torque control
(DTC) of the BLDC motor is important in many applications. In this paper
BLDC motor is controlled with DTC using PI, PID and Fuzzy logic control.
The comparison of the performance of the motor is analyzed with the Matlab
simulation software.
performance assessment of a wind turbine using fuzzy logic and artificial net...INFOGAIN PUBLICATION
This paper makes a comparison between two control methods for maximum power point tracking (MPPT) of a wind turbine modules using Permanent Magnet Synchronous Generators(PMSG) under fixed and different wind condition: the Fuzzy Logic (FL) and the Artificial Neural Network control (ANN). Both techniques have been simulated and analyzed by using Matlab/Simulink software. The simulated power transitions and the power tracking time realized by the fuzzy logic controller and the neural network controller has been evaluated in comparison with Tip Speed Ratio controller (TSR).
end to end delay performance analysis of video conferencing over lteINFOGAIN PUBLICATION
Mental development to use the data, such as multimedia, video and online games led to the development of a technique called LTE long term evolution. The goal of this paper is to analyze the quality of service (QoS) performance and its effects when video is streamed over LTE .Using OPNET (Optimized Network Engineering Tool). the performance can be simulated having Different scenarios for video conferencing . in addition to we also measured the performance of packet End-to-End delay .
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.
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.
Performance Comparison of Conventional Controller with Fuzzy Logic Controller...ijeei-iaes
It is often difficult to develop an accurate mathematical model of DC motor due to unknown load variation, unknown and unavoidable parameter variations or nonlinearities due to saturation temperature variations and system disturbances. Fuzzy logic application can handle such nonlinearities so that the controller design is fundamentally robust which is not possible in conventional controllers. The knowledge base of a fuzzy logic controller (FLC) encapsulates expert knowledge and consists of the Data base (membership functions) and Rule-Base of the controller. Optimization of both these knowledge base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater numbers of inputs, more formal methods of knowledge base optimization are required. In this work, we study the challenging task of controlling the speed of DC motor. The feasibility of such controller design is evaluated by simulation in the MATLAB/Simulink environment. In this study Conventional Proportional Integral Derivative controller, Fuzzy logic controller using a chopper circuit and Fuzzy tuned PID controller are analyzed and compared. Simulation software like MATLAB with Simulink has been used for modeling and simulation purpose. The performance comparison of conventional controller with Fuzzy logic controller using chopper circuit and Fuzzy tuned PID controller has been done in terms of several performance measures Such as Settling time, Rise time and Overshoot.
Speed controller design for three-phase induction motor based on dynamic ad...IJECEIAES
Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.
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
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.
Performance Analysis of Direct Torque Controlled BLDC motor using Fuzzy LogicIAES-IJPEDS
The Brushless DC motor (BLDC) control is used in many of the applications
as it is small in size and with low power which can drive in high speed and
lighter compared to other motors.The electric vehicles are built with BLDC
motors and also in ships, aerospace etc., The control of BLDC motors is done
with sensors like hall effect sensor for sensing the positions. The speed
control can be done with normal PI and PID controllers. Direct torque control
(DTC) of the BLDC motor is important in many applications. In this paper
BLDC motor is controlled with DTC using PI, PID and Fuzzy logic control.
The comparison of the performance of the motor is analyzed with the Matlab
simulation software.
performance assessment of a wind turbine using fuzzy logic and artificial net...INFOGAIN PUBLICATION
This paper makes a comparison between two control methods for maximum power point tracking (MPPT) of a wind turbine modules using Permanent Magnet Synchronous Generators(PMSG) under fixed and different wind condition: the Fuzzy Logic (FL) and the Artificial Neural Network control (ANN). Both techniques have been simulated and analyzed by using Matlab/Simulink software. The simulated power transitions and the power tracking time realized by the fuzzy logic controller and the neural network controller has been evaluated in comparison with Tip Speed Ratio controller (TSR).
end to end delay performance analysis of video conferencing over lteINFOGAIN PUBLICATION
Mental development to use the data, such as multimedia, video and online games led to the development of a technique called LTE long term evolution. The goal of this paper is to analyze the quality of service (QoS) performance and its effects when video is streamed over LTE .Using OPNET (Optimized Network Engineering Tool). the performance can be simulated having Different scenarios for video conferencing . in addition to we also measured the performance of packet End-to-End delay .
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.
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.
Performance Comparison of Conventional Controller with Fuzzy Logic Controller...ijeei-iaes
It is often difficult to develop an accurate mathematical model of DC motor due to unknown load variation, unknown and unavoidable parameter variations or nonlinearities due to saturation temperature variations and system disturbances. Fuzzy logic application can handle such nonlinearities so that the controller design is fundamentally robust which is not possible in conventional controllers. The knowledge base of a fuzzy logic controller (FLC) encapsulates expert knowledge and consists of the Data base (membership functions) and Rule-Base of the controller. Optimization of both these knowledge base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater numbers of inputs, more formal methods of knowledge base optimization are required. In this work, we study the challenging task of controlling the speed of DC motor. The feasibility of such controller design is evaluated by simulation in the MATLAB/Simulink environment. In this study Conventional Proportional Integral Derivative controller, Fuzzy logic controller using a chopper circuit and Fuzzy tuned PID controller are analyzed and compared. Simulation software like MATLAB with Simulink has been used for modeling and simulation purpose. The performance comparison of conventional controller with Fuzzy logic controller using chopper circuit and Fuzzy tuned PID controller has been done in terms of several performance measures Such as Settling time, Rise time and Overshoot.
Speed controller design for three-phase induction motor based on dynamic ad...IJECEIAES
Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.
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
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.
This paper presents the comparative performances of Indirect Field Oriented Control (IFOC) for the three-phase induction motor. Recently, the interest of widely used the induction motor at industries because of reliability, ruggedness and almost free in maintenance. Thus, the IFOC scheme is employed to control the speed of induction motor. Therefore, P and PI controllers based on IFOC approach are analyzed at differences speed commands with no load condition. On the other hand, the PI controller is tuned based on Ziegler-Nichols method by using PSIM software which is user-friendly for simulations, design and analysis of motor drive, control loop and the power converter in power electronics studies. Subsequently, the simulated of P controller results are compared with the simulated of PI controller results at difference speed commands with no load condition. Finally, the simulated results of speed controllers are compared with the experimental results in order to explore the performances of speed responses by using IFOC scheme for three-phase induction motor drives.
This paper presents a new Piecewise Affine Proportional-Integral (PA-PI) controller for angular velocity tracking of a buck converter generated dc motor. A Safe Experimentation Dynamics (SED) algorithm is employed as a data-driven optimization tool to find the optimal PA-PI controller parameters such that the integral square of error and input are reduced. The essential feature of the PA-PI controller is that the parameters of proportional and integral gains are adaptive to the error variations according to the Piecewise Affine (PA) function. Moreover, the proposed PA function is expected to provide better control accuracy than the other existing variable structure PID controller. In order to verify the effectiveness of the PA-PI controller, a widely known buck converter generated dc motor is considered. The performances of the proposed controller are observed in terms of the integral square of error and input, and the responses of the angular velocity and duty ration input. The simulation results verify that the proposed PA-PI controller yields higher control accuracy than the other existing controllers of buck converter generated dc motor.
Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Ind...IJECEIAES
In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations.
In this paper, several methods are developed to control the brushless DC (BLDC) motor speed. Since it is difficult to get a good showing by utilizing classical PID controller, the Dynamic Wavelet Neural Network (DWNN) is the proposed work in this paper, with parallel PID controller to obtain an novel controller named DWNN-PID controller. It collects the artificial neural ability of its networks for imparting from motor of BLDC with drive system and the ability of identification for the wavelet decomposition and control of the dynamic system furthermore to have ability for adapting and self-learning. The suggested controller method is utilizing to control the speed of BLDC motor of which supply a better showing than utilizing classical controllers with a wide range of control. The proposed controller parameters are matched continuously using Particle Swarm Optimization (PSO) algorithm. The simulation results based on proposed DWNN-PID controller demonstrate a superior in the stability and performance compared at utilizing classical WNN-PID and conventional PID controllers. The simulation results are accomplished using Matlab/Simulink. It shows that the proposed control scheme has a superior performance.
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).
Dynamic Simulation of Induction Motor Drive using Neuro Controlleridescitation
Induction Motors are widely used in Industries, because of the low maintenance
and robustness. Speed Control of Induction motor can be obtained by maximum torque and
efficiency. Apart from other techniques Artificial Intelligence (AI) techniques, particularly
the neural networks, improves the performance & operation of induction motor drives. This
paper presents dynamic simulation of induction motor drive using neuro controller. The
integrated environment allows users to compare simulation results between conventional,
Fuzzy and Neural Network controller (NNW).The performance of fuzzy logic and artificial
neural network based controller's are compared with that of the conventional proportional
integral controller. The dynamic Modeling and Simulation of Induction motor is done using
MATLAB/SIMULINK and the dynamic performance of induction motor drive has been
analyzed for artificial intelligent controller.
This paper deal with the problem in speed controller for Indirect Field Oriented Control of Induction Motor. The problem cause decrease performance of Induction Motor where it widely used in high-performance applications. In order decrease the fault of speed induction motor, Takagi-Sugeno type Fuzzy logic control is used as the speed controller. For this, a model of indirect field oriented control of induction motor is built and simulating using MATLAB simulink. Secondly, error of speed and derivative error as the input and change of torque command as the output for speed control is applied in simulation. Lastly, from the simulation result overshoot is zero persent, rise time is 0.4s and settling time is 0.4s. The important data is steady state error is 0.01 percent show that the speed can follow reference speed. From that simulation result illustrate the effectiveness of the proposed approach.
Speed Control of Induction Motor by Using Intelligence TechniquesIJERA Editor
This paper gives the comparative study among various techniques used to control the speed of three phase induction motor. In this paper, indirect vector method is used to control the speed of Induction motor. Firstly Simulink Model is developed by using MATLAB/ Simulink software. PI controller, Fuzzy PI Hybrid controller, Genetic Algorithm (GA) are the techniques involved in control Induction motor and the results are compared. By converting three phase supply currents coming from stator to Flux and Torque components of current the speed responses such as rise time, overshoot, settling time and speed regulation at load have been observed and compared among the techniques. The PI controller parameters defined by an objective function are calculated by using Genetic Algorithms presented good performance compared to Fuzzy PI Hybrid controller which has parameters chosen by the human operator.
To design and implementation of variable and constant with no load for induction motor (IM) that is the goal in this work. This paper was including three parts, first the simulation model with no load for IM, Second the simulation model with constant load for IM, Third the simulation model with variable load for IM. In addition, this work includes comparative between two different controllers (PI and fuzzy logic control (FLC). The simulation results clearly the implementation of variable and constant with no load for IM. The simulation response of the system achieves better results when choosing to use type fuzzy-PI controller technique comparison with conventional PI controller and improve the performance of the system at different operation conditions.
Abstract - This paper addresses some of the potential benefits of
using fuzzy logic controllers to control an inverted pendulum
system. The stages of the development of a fuzzy logic controller
using a four input Takagi-Sugeno fuzzy model were presented.
The main idea of this paper is to implement and optimize fuzzy
logic control algorithms in order to balance the inverted
pendulum and at the same time reducing the computational time
of the controller. In this work, the inverted pendulum system was
modeled and constructed using Simulink and the performance of
the proposed fuzzy logic controller is compared to the more
commonly used PID controller through simulations using Matlab.
Simulation results show that the Fuzzy Logic Controllers are far
more superior compared to PID controllers in terms of overshoot,
settling time and response to parameter changes.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
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/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Dz36755762
1. Yellaiah.Ponnam et al Int. Journal of Engineering Research and Applications
ISSN: 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.755-762
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
Ziegler-Nichols (Z-N) Based PID Plus Fuzzy Logic Control (FLC) For
Speed Control of A Direct Field-Oriented Induction Motor (DFOIM)
Srinivas.Singirikonda1, Yellaiah.Ponnam2, Sravan Kumar.Palarapu3
Assistant professor Dept of EEE in SIET (JNTU-H), Ibrahimpatanam, Hyderabad, India
Assistant Professor Dept of EEE in GNIT (JNTU-H), Hyderabad, India
Sr.Assistant Professor Dept of EEE in ASTRA (JNTU-H), Hyderabad, India
Abstract
The induction motor is having non-linear torque and internal impedance characteristics. In this paper, a Ziegler-Nichols
(Z-N) based PID plus fuzzy logic control (FLC) scheme is proposed for speed control of a direct field-oriented induction
motor (DFOIM). The Z-N PID is adopted because its parameter values can be chosen using a simple and useful rule of
thumb. The FLC is connected to the PID controller for enhancing robust performance in both dynamic transient and
steady-state periods. The FLC is developed based on the output of the PID controller, and the output of the FLC is the
torque command of the DFCIM. The complete closed-loop speed control scheme is implemented for the laboratory 0.14hp squirrel-cage induction motor. Experimental results demonstrate that the proposed Z-N PID+FLC scheme can lead to
desirable robust speed tracking performance under load torque disturbances.
Index Terms: Induction Motor, Ziegler-Nichols (Z-N) based PID plus fuzzy logic control (FLC), Z-N PID, direct fieldoriented induction motor (DFOIM)
I. Introduction
In recent years, field-oriented induction machine
(FOIM) drives [1] have been increasingly utilized in motion
control applications due to easy implementation and low cost.
Besides, they have the advantage of decoupling the torque and
flux control, which makes high servo quality achievable.
However, the decoupling control feature can be adversely
affected by load disturbances and parameter variations in the
motor so that the variable-speed tracking performance of an IM
is degraded. In general, both conventional PI and PID
controllers have the difficulty in making the motor closely
follow a reference speed trajectory under torque disturbances.
In this regard, an effective and robust speed controller design is
needed.
In [2]-[8], fuzzy-logic-based intelligent controllers
have been proposed for speed control of FOIM drives. Those
intelligent controllers are associated with adaptive gains due to
fuzzy inference and knowledge base. As a result, they can
improve torque disturbance rejections in comparison with best
trial-and-error PI or PID controllers. Nonetheless, no
performance advantages of intelligent controllers in
combination with a PI or PID controller are investigated in [2][8].
Motivated by the successful development and
application in [2]-[8], we propose a hybrid PID+ fuzzy
controller consisting of a PID controller and a fuzzy logic
controller (FLC) in a serial arrangement for speed control of
FOIM drives, more specifically, direct field-oriented IM
(DFOIM) drives. The Ziegler-Nichols (Z-N)) method in [9] is
adopted for designing a PID controller (denoted as “the Z-N
PID”) because its design rule is simple and systematic. We
next design a FLC carrying out fuzzy tuning of the output of
the Z-N PID controller to issue adequate torque commands.
www.ijera.com
Based on a simulation model of the DFOIM drives
incorporating the proposed controller, experiments are set up in
a Mat lab /SIMULINK environment and implemented in real
time using the MRC-6810 analog-to-digital (AD)/ digital-toanalog (DA) servo control card together with a DSP electronic
controller. The results show that the incorporation of the
proposed controller into the DFOIM drives can yield superior
and robust variable-speed tracking performance.
II. Physical Phenomenon of Induction Motor and
Control Structure
In this section, we introduce the DFOIM drive shown
in Fig. 1. The dynamics of an induction motor can be described
by synchronously rotating reference frame direct-quadrature
(d-q) equations [10] as
--- (1)
--- (2)
--- (3)
--- (4)
Where the notational superscript “e” stands for the
synchronous reference frame:
,
,
,
,and
stand
for the d-axis and the q-axis stator voltages, stator currents and
rotor currents; Rs , Rr , Ls and Lr denote the resistances and
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self-inductances of the stator and the rotor; Lm denotes the
mutual inductance; Te and TL represent the electromagnetic
and external force load torques, respectively; J m and B m are
the rotor inertia and the coefficient of viscous damping,
respectively; ωr and ωrm denote the rotor and motor mechanical
speeds;
stands for electrical angular velocity; N is the
number of poles of the motor mechanical speed; p stands for
the differential operator (d /dt) . The notational superscript “s”
in Fig. 1 stands for stationary reference frame. For a DFOIM
drive, the flux has to fall entirely on d-axis. To control the
speed of the IM, the speed controller of the DFOIM drive
transforms the speed error signal e into an appropriate
electromagnetic torque command .
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III. Dynamic Modeling of Induction Motor
The per phase equivalent circuit of the machine is
only valid in steady-state condition. When studying steady
state performance of the machine, the electrical transients are
neglected during load changes and stator frequency variations.
In an adducible-speed drive, the machine normally constitutes
an element with in a feedback loop, and therefore its transient
behavior has to be taken in to consideration. Besides, highperformance drive control, such as vector or field-oriented
control is based on the dynamic d-q model of the machine.
Therefore, to understand vector control principles, a good
understanding of the d-q model is mandatory.
Fig.1.The block diagram of speed control of a DFOIM
fuzzy process, we only employ three input
3.1. Proposed Hybrid PID Plus Fuzzy Control for Test
membership functions
,
and
shown in Fig. 3 to
System
map a crisp input to a fuzzy set with a degree of certainty
The structure of the proposed controller is shown in
where x = g(t) or Δg(t) with g(t) = K1 f (t) and Δg(t) = K2Δf
Fig. 2. The steps to acquire the Z-N PID [9] for speed control
(t) . Those three membership functions are chosen because of
of the DFOIM in Fig. 1 are given as follows. First, we use a
their simplicity for computation since a large number of
fixed step input ω
and a linear proportional speed
membership functions and rules can cause high
controller. The proportional gain of the speed controller is
computational burden for a fuzzy controller. For any x∈ N
increased until the
where N denotes the interval (−∞,0] , its corresponding
linguistic value is ‘N’. Moreover, for any x∈ P where P
DFOIM reaches its stability limit. As a result, we obtain the
denotes the interval (0,∞) , its corresponding linguistic value
period of the critical oscillation at the stability limit of the
is ‘P’. For any x∈Z where Z denotes the interval [−b, b] , its
DFOIM with the critical proportional gain Ku . Next, the
corresponding linguistic value is ‘Z’. The membership
values of the parameters Kp , TI , TD are given by
functions
,
and
are given by
--- (5)
--- (6)
time and
Where the proportional is gain;
is the derivative time. In the
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---- (8)
--- (7)
is the integral
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i. If
--- (9)
∈
ii. If
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∈
iii. If
∈
∈
--- (10)
iv. If
The fuzzy inference engine, based on the input
fuzzy sets in combination with the expert’s experience, uses
adequate IF-THEN rules in the knowledge base to make
decisions and produces an implied output fuzzy set u . For
this particular application, the proposed IF-THEN fuzzy rule
base is shown in Table 1 and is described as follows:
∈
∈
v. If
∈
∈
--- (11)
Moreover, the Mamdani-type min operation for
fuzzy inference is employed in this study.
Fig.2.The block diagram of the proposed controller
In the de fuzzification process, we employ the‘center
of mass’ defuzzification method for transforming the implied
output fuzzy set into a crisp output, and obtain
∈
∈
∈
∈
--- (12)
Fig.3. Membership functions with x=g(t)
Table 1.Fuzzy rule base
∈
∈
∈
∈
∈
∈
∈
∈
---- (13)
The output of the fuzzy controller is given by
---- (14)
3.2.
Proposed
Control
Strategy:
d-q
Model
Transformation
The dynamic performance of an ac machine is
somewhat complex because the three-phase rotor windings
move with respect to the three-phase stator windings as
shown in figure 3.1(a).
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The 1930s, H.C.Stanley showed that time-varying
inductances in the voltage equations of the induction machine
due to electric circuits in relative motion can be eliminated by
transforming the rotor variables to variables associated with
fictitious stationary windings. In this case, the rotor variables
are transformed to a stationary reference frame fixed on the
stator. Later, G.Kron proposed a transformation of both rotor
and stator variables to a synchronously rotating reference
frame that moves with the rotating magnetic field.
D.S.Brereton proposed a transformation of stator to a rotating
reference frame that is fixed on the rotor. In fact, it was
shown later by Krause and Thomas that time-varying
inductances can be eliminated by referring the stator and rotor
variables to a common reference frame which may rotate at
any speed (arbitrary reference frame). Without going deep in
to rigor of machine analysis, we will try to develop a dynamic
machine model in a synchronously rotating and stationary
reference frames.
Fig.4. (a) Coupling effect in three-phase (b) Equivalent
two-phase machine Stator and rotor windings
Basically, it can be looked on a s a transformer with
a moving secondary, where the coupling coefficients between
the stator and rotor phases change continuously with the
change of rotor position θr. The machine can be described by
differential equations with time-varying mutual inductances,
but such a model tends to be very complex. Note that a threephase machine can be represented by an two-phase machine
as shown in 3.1(b), where
correspond to stator direct
and quadrature axes, and
correspond to rotor direct
and quadrature axes.
Although, it is somewhat simple, the problem of time-varying
parameters still remains. R.H. Park, in1920s, proposed a new
theory of electric machine analysis to solve this problem. He
formulated a change of variables, which, in effect, replaced
the variables (voltages, currents and flux linkages) associated
with the stator windings of synchronous machine with
variables associated with fictitious windings rotating with the
rotor with synchronous speed. Essentially, he transformed, or
referred, the stator variables to a synchronously rotating
reference frame fixed in the rotor. With such transformation
(called park’s transformation), he showed that all the timevarying inductances that occur due to an electric circuit in
relative motion and electric circuits with varying magnetic
reluctances can be eliminated. Later, in.
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Consider a symmetrical three-phase induction
machine with stationary as-bs-cs axes at 2Π/3 angle apart, as
shown in figure 3.2. Our goal is to transform the three-phase
stationary reference frame (as-bs-cs) variables into two –
phase stationary reference frame (ds-qs) variables and then
these to synchronously rotating reference frame (de-qe), and
vice versa. Assume that the ds-qs axes are oriented at θ angle,
as shown in figure 3.2. The voltages vdss and vqss can be
resolved into as-bs-cs components and ca n be represented in
matrix form as
v as cos
o
v bs = cos( 120 )
v cs
cos( 120 o )
1 v qs
s
sin( 120 o ) 1 v ds
s
sin( 120 o ) 1 v os
s
sin
--- (15)
The corresponding inverse relation is
vqs s
s
vds
s
vos
=
cos
2
sin
3
0.5
cos( 120o )
sin( 120o )
0.5
cos( 120o )
sin( 120o )
0.5
v as
v bs
v cs
---- (16)
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Where Voss is added as the zero sequence
component, which may or may not be present. We have
considered voltage as the variable. The current and flux
linkages can be transformed by similar equations. It is
convenient to set θ = 0, so that the q s-axis is aligned with
the as-axis. Ignoring the zero sequence components, the
transformation relations can be simplified as
Vas = Vqss
Vbs = - (1/2)Vqss - ( √3/2) Vdss
Vcs = - (1/2) Vqss + ( √3/2) Vdss
Vqss= (2/3) Vas –(1/3) Vbs – (1/3) Vcs
--- (17)
--- (18)
--- (19)
--- (20)
The synchronously rotating de-qe axes, which rotate
at synchronous speed ωe with respect to the ds-qs axes and
the angle θe = ωet. Two-phase ds-qs windings are
transformed into the hypothetical windings mounted on the
de-qe axes. The voltages on the ds-qs axes can be converted
(or resoled) into the de-qe frame.
IV. MATLAB/SIMULINK for Proposed Test
System
Usually, when an electrical machine is simulated in
circuit simulators like PSPICE, its steady state model is
used, but for electrical drive studies, the transient behavior
is also important. One advantage of SIMULINK over circuit
simulators is the ease in modeling the transients of electrical
machines and drives and to include drive controls in the
simulation. As long as the equations are known, any drive
or control algorithm can be modeled in SIMULINK.
However, the equations by themselves are not always
enough; some experience with differential equation solving
is required. SIMULINK induction machine models are
available in the literature [I-3], but they appear to be black
boxes with no internal details. Some of them [1-3]
recommend using S- functions, which are software source
codes for SIMULINK blocks. This technique does not fully
utilize the power and ease of SIMULINK because Sfunction programming knowledge is required to access the
model variables. S- Functions run faster than discrete
SIMULINK blocks, but SIMULINK models can be made to
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run faster using “accelerator” functions or producing standalone SIMULINK models. Both of these require additional
expense and can be avoided if the simulation speed is not
that critical. Another approach is using the SIMULINK
Power System Block set [4] that can be-purchased with
SIMULINK. This block set also makes use of S-functions
and is not as easy to work with as the rest of the
SIMULINK blocks Reference [5] refers to an
implementation approach similar to the one in this paper but
fails to give any details. In this paper, a modular, easy to
understand SIMULINK induction motor model is described.
With the modular system, each block solves one of the
model equations; therefore, unlike black box models, all of
the machine parameters are accessible for control and
verification purposes. SIMULINK induction machine model
discussed in this paper has been featured in a recent
graduate level text book [6].
The inputs of a squirrel cage induction machine are
the three-phase voltages, their fundamental frequency, and
the load torque. The outputs, on the other hand, are the three
phase currents, the electrical torque, and the rotor speed.
The d-q model requires that all the three-phase variables
have to be transformed to the two-phase synchronously
rotating frame. Consequently, the induction machine model
will have blocks transforming the three-phase voltages to
the d-q frame and the d-q currents back to three-phase. The
induction machine model implemented in this paper is
shown in Fig. 2. It consists of five major blocks. The o-n
conversion, abc-syn conversion, syn-abc conversion, unit
vector calculation, and the induction machine d-q model
blocks. The following subsections will explain each block.
4.1. Induction machine d-q model
The inside of this block where each equation from
the induction machine model is implemented in a different
block. First consider the flux linkage state equations
because flux linkages are required to calculate all the other
variables. These equations could be implemented using
SIMULINK “State-space” block, but to have access to each
point of the model, implementation using discrete blocks is
preferred.
Figure.5.Complete Induction Motor SIMULINK model
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Figure.6. SIMULINK Implementation of Induction motor d-q model
V. Results and Discursions
A model of test system is developed using the MATLAB
/SIMULINK software. The parameter values of the 0.14-hp
squirrel-cage induction motor are given as follows:
Figure.8. Speed of Induction with Z-N PID+Fuzzy
Controller
From figure 7&8 shows the variation between speed characteristics
of Induction motor with ZN PID Controller and ZN PID Combined
with Fuzzy Controller. Meanwhile, the speed curve which shown
figure.7 contains speed of 2000 rpm at 4.8 sec with ZN PID
Controller and by using ZN PID+Fuzzy Controller, the same speed
achieved in 4.2 sec. So, the ZN PID+ Fuzzy Controller will show
the better performance speed characteristic in less settling time.
Figure.7. Speed of Induction motor with Z-N PID
Controller
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Figure.9. Torque per phase of IM with Z-N PID
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[7]
[8]
Figure.10. Torque per phase of IM with Z-N
PID+Fuzzy Controller
From figure 9&10 shows the variations for torque responses of
DFIM operating with PID and PID+Fuzzy Controller. Here from
the very first figure torque response is 0.81N-m for 15sec
duration with PID Controller. And this load torque per phase
was modified and improved, compensated to 0.69N-m in 12sec.
Hence simultaneously, the PID combined with FLC will
improve the DFIM pay load torque responses.
[9]
[10]
[11]
VI. Conclusion
In this paper, a novel hybrid modified Z-N PID+FLC-based
speed control of a DFOIM has been presented. The proposed
controller has exhibited the combined advantages of a PID
controller and a FLC. Specifically, it can improve the stability,
the transient response and load disturbance rejection of speed
control of a DFOIM. The complete DFOIM drive incorporating
the proposed controller has been implemented in real time
using a MRC-6810 AD/DA servo control card for the Nikki
Denso NA21-3F 0.14Hp induction motor. The fuzzy logic and
only with three membership functions are used for each input
and output for low computational burden, which can achieve
satisfactory results. Simulation and experiment results have
illustrated that the proposed controller scheme has a good and
robust tracking performance. As suggested topology says that a
modified Z-N PID can perform better than a Z-N PID, our
future effort will focus on how to further improve the
performance of the proposed controller herein by incorporating
a modified Z-N PID.
[12]
[13]
[14]
[15]
[16]
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ABOUT AUTHORS:
Srinivas.Singirikonda, Asst.Professor
Received M.Tech degree in Control
Systems in Dept. of Electrical and
Electronics Engineering, JNTU
Hyderabad. He is currently working
as
Asst. Professor in EEE
Department of Siddhartha Institute
of
Engineering&
Technology
,Hyderabad, His is doing currently
research in Fuzzy logic controllers,
Power electronics, FACTS and
PLCs
Sravan Kumar.Palarapu, Asst.Professor
Received B.Tech degree in Electrical
and Electronics Engineering from the
University of JNTU, M.Tech in Power
Electronics from the University of
JNTU-Hyderabad. He is currently Sr.
Asst. Professor in EEE Department of
Aurora’s Scientific, Technological and
research Academy, Hyderabad, His
currently research interests include
control system, Power electronics,
PLCs.
Yellaiah. Ponnam, Asst.Professor
Received M.Tech degree in Control
Systems in Dept. of Electrical and
Electronics
Engineering,
JNTU
Hyderabad. He is currently working as
Asst. Professor in EEE Department of
Guru Nanak Institute of Technology
,Hyderabad, His is doing currently
research in Real time application in
control sytems,Fuzzy logic controller,
Power electronic drives and FACTS ,
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