In this study, a control strategy is presented to control the position and the feed rate of a table of a milling
machine powered by three-phase induction motor, when machining pieces constituted by different types of
materials: steel, brass and nylon. For development of the control strategy, the vector control technique was
applied to drive the three-phase induction machines. The estimation of the electromagnetic torque of the
motor was used to determine the machining feed rate for each type of material. The speed control was
developed using fuzzy logic Takagi-Sugeno (TS) model and the estimation of the electromagnetic torque
using the artificial neural network (ANN) of the least mean square (LMS) algorithm type. The induction
motor was fed by a three-phase voltage inverter hardware driven by a digital signal processor (DSP).
Experimental results are presented.
Positioning Error Analysis and Compensation of Differential Precision WorkbenchIJRES Journal
Positioning error is a widely problem exists in mechanism, the important factors affecting machining
precision. In order to reduce the error caused by positioning problem processing, based on the differential
workbench as the research object, using the method of theoretical analysis and experimental verification, the
analysis of positioning error mechanism and source of complete differential precision workbench error
compensation, improve the accuracy of the device, provides a method for the application of modern machine
tools. table.
The characteristics of TB6600 motor driver in producing optimal movement for ...TELKOMNIKA JOURNAL
This study describes the use of the TB6600 motor driver for the stepper motor on CNC machine. Based on the analysis of the performance of the TB6600 motor driver, in order to produce an optimal stepper motor of Nema23 on CNC machine, three stepper motors are needed as the CNC engine drives for the X, Y and Z axeo be connected to the TB6600 motor driver. The motor is then controlled by Raspberry Pi via Mach3 Interface Board of Breakout Board. The softness of motion and safe working temperature for the stepper motor of Nema23 on CNC machine are obtained by varying the control of the micro-step switches and controlling current switches. The results show that 32 steps of micro-step control produce smoother resonance and movement than smaller micro-steps. In addition, the current control of 1 A generates the best motor driver output with a lower temperature for all three stepper motors.
Based on visual basic differential workbench system design and implementation...eSAT Journals
Abstract
In this paper, we take the equipment precision differential bench which homemade by Shanghai University of Engineering and Technology for the study, and use the Visual Basic for precision motion system of differential table design and secondary development. Through experiments, we can get the change in position under different sports differential table, through the measurement of data analysis, we can get in the form of differential motion error table for further compensation error of the foundation.
Keywords: Visual Basic, Differential, The error analysis
Estimation of motor inertia and friction components is a complex and challenging task in motion control applications where small size DC motors (<100W) are used for precise control. It is essential to estimate the accurate friction components and motor inertia, because the parameters provided by the manufacturer are not always accurate. This research proposes a Sensorless method of determining DC motor parameters, including moment of inertia, torque coefficient and frictional components using the Disturbance Observer (DOB) as a torque sensor. The constant velocity motion test and a novel Reverse Motion Acceleration test were conducted to estimate frictional components and moment of inertia of the motor. The validity of the proposed novel method was verified by experimental results and compared with conventional acceleration and deceleration motion tests. Experiments have been carried out to show the effectiveness and viability of the estimated parameters using a Reaction Torque Observer (RTOB) based friction compensation method.
NEW APPROACH FOR COMPUTER-AIDED STATIC BALANCING OF TURBINES ROTORSBarhm Mohamad
The balancing operation consists in improving the distribution of the rotor masses so that the free centrifugal forces around the rotor axis, imposed by the manufacturer, do not exceed the tolerances allowed by the standards. In this paper we propose algorithms for the distribution of the turbine blades from data from an electronic scale which allows to measure the static moment of the blades, these algorithms aim to find the correction weight and the angle of position of the correction mass, we also propose a simulation of the distribution of the blades of a turbine to get an idea on the assembly. This operation is necessary in the case of a repair of the rotors or in the assembly of the new flexible rotors. Using a MATLAB calculation code.
Positioning Error Analysis and Compensation of Differential Precision WorkbenchIJRES Journal
Positioning error is a widely problem exists in mechanism, the important factors affecting machining
precision. In order to reduce the error caused by positioning problem processing, based on the differential
workbench as the research object, using the method of theoretical analysis and experimental verification, the
analysis of positioning error mechanism and source of complete differential precision workbench error
compensation, improve the accuracy of the device, provides a method for the application of modern machine
tools. table.
The characteristics of TB6600 motor driver in producing optimal movement for ...TELKOMNIKA JOURNAL
This study describes the use of the TB6600 motor driver for the stepper motor on CNC machine. Based on the analysis of the performance of the TB6600 motor driver, in order to produce an optimal stepper motor of Nema23 on CNC machine, three stepper motors are needed as the CNC engine drives for the X, Y and Z axeo be connected to the TB6600 motor driver. The motor is then controlled by Raspberry Pi via Mach3 Interface Board of Breakout Board. The softness of motion and safe working temperature for the stepper motor of Nema23 on CNC machine are obtained by varying the control of the micro-step switches and controlling current switches. The results show that 32 steps of micro-step control produce smoother resonance and movement than smaller micro-steps. In addition, the current control of 1 A generates the best motor driver output with a lower temperature for all three stepper motors.
Based on visual basic differential workbench system design and implementation...eSAT Journals
Abstract
In this paper, we take the equipment precision differential bench which homemade by Shanghai University of Engineering and Technology for the study, and use the Visual Basic for precision motion system of differential table design and secondary development. Through experiments, we can get the change in position under different sports differential table, through the measurement of data analysis, we can get in the form of differential motion error table for further compensation error of the foundation.
Keywords: Visual Basic, Differential, The error analysis
Estimation of motor inertia and friction components is a complex and challenging task in motion control applications where small size DC motors (<100W) are used for precise control. It is essential to estimate the accurate friction components and motor inertia, because the parameters provided by the manufacturer are not always accurate. This research proposes a Sensorless method of determining DC motor parameters, including moment of inertia, torque coefficient and frictional components using the Disturbance Observer (DOB) as a torque sensor. The constant velocity motion test and a novel Reverse Motion Acceleration test were conducted to estimate frictional components and moment of inertia of the motor. The validity of the proposed novel method was verified by experimental results and compared with conventional acceleration and deceleration motion tests. Experiments have been carried out to show the effectiveness and viability of the estimated parameters using a Reaction Torque Observer (RTOB) based friction compensation method.
NEW APPROACH FOR COMPUTER-AIDED STATIC BALANCING OF TURBINES ROTORSBarhm Mohamad
The balancing operation consists in improving the distribution of the rotor masses so that the free centrifugal forces around the rotor axis, imposed by the manufacturer, do not exceed the tolerances allowed by the standards. In this paper we propose algorithms for the distribution of the turbine blades from data from an electronic scale which allows to measure the static moment of the blades, these algorithms aim to find the correction weight and the angle of position of the correction mass, we also propose a simulation of the distribution of the blades of a turbine to get an idea on the assembly. This operation is necessary in the case of a repair of the rotors or in the assembly of the new flexible rotors. Using a MATLAB calculation code.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
The application of the direct torque control strategy for induction machine drives is mainly characterized by torque and flux distortions caused by voltage vector limitation. The goal of this paper is to perform the conventional DTC induction machine drives and reduce ripples of both flux and torque response. The proposed contribution is based on the control of the DC output side of the rectifier feeding the voltage source inverter by means of PI controller in order to adapt the voltage vector used in typical DTC switching table. Mathematic models are built using MATLAB Simulink and programming environment; the simulation results show the difference between the proposed method and classical DTC.
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.
Investigating the Intelligent Methods of Loss Minimization in Induction MotorsTELKOMNIKA JOURNAL
Induction motors are widely used in industry. Given the increasing demand for electric machines in
different industries, optimization of these machines to achieve a high efficiency with low cost is of utmost
importance. Loss-minimization in motor is done in three ways: 1) optimizing motor selection and design; 2)
improving motor power supply waveforms; and 3) using appropriate controlling methods in drives. Often,
inductive motors provide the maximum efficiency in their nominal load. In most applications it is necessary for
a motor to work in light loads for a long time, e.g. in conveyors, elevators, etc. In these conditions, the
machine load is not the nominal load, and a higher percentage of the input power is lost. So, in the case of
variable load, the first and second methods cannot increase the efficiency; but the third method provides a
large flexibility in decreasing motor losses. In this paper, the application of the third method in lossminimization
is reviewed. These motor losses are mostly related to the controlling strategy and basically
occur in light-load conditions. There are various strategies to decrease this kind of losses, which are generally
divided into two categories: classic methods and intelligent methods. In this paper, first the classic methods,
including losses model control (LMC), flux control as a function of torque and search control (SC), are
discussed. Then the intelligent methods, such as genetic algorithm, PSO, fuzzy logic and artificial neural
network are investigated. This paper is presented while the last methods of efficiency improvement are being
investigated and each method is described briefly.
Designing the virtual model of a mechatronic micro positioning and micro-meas...eSAT Journals
Abstract This paper aims is to present the realization of a virtual model, as well as the experimental model for a mechatronic micro-positioning and micro-measuring system on two axes, OX, and OZ respectively. The paper also includes experimental results on measuring the gripping force developed between the fingers of an electrical micro-gripper, with two fingers, incorporated into the experimental model of a mechatronic micro-positioning system. Index Terms: flexible mechatronic system, micro-positioning, micro-gripper
Three phase induction motor Induction is one of the widest spread motor due to its
robustness, simple construction, no need for complex circuits for starting. With several
available speed control techniques, this paper presents a new Proportional-Integral (PI)
controller and Artificial Neural Network (ANNs) control system based on vector control
scheme. MATLAB/SIMULINK software may be used to create a 3phase induction engine
model. To achieve the effectiveness of the controller, the system is subjected to external
disturbance. Experimental results are presented and satisfied with the controller results.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
DSP-Based Sensorless Speed Control of a Permanent Magnet Synchronous Motor us...IJPEDS-IAES
In this paper, experimental results of 3-phase permanent magnet synchronous motor (PMSM) sensorless speed control are presented. To estimate the rotor position, a sliding mode current observer (SMCO) was implemented. This observer estimates the back emfs of the motor in the stationary reference frame using only the measured voltages and currents of the motor. These emfs were utilized to obtain the rotor position. The speed of the motor was calculated by differentiating the rotor position angle. The stability of the proposed SMCO was verified using Lyapunov method to determine the observer gain. The saturation function was adopted in order to reduce the chattering phenomenon caused by the SMCO. A vector control method was employed to achieve the sensorless drive system. The control application was developed in C/C++ language and implemented using the Texas Instruments TMS320LF2812 digital signal processor (DSP). This new processor enables intelligent control for motors. We used to test the drive the MCK2812 which is a professional development kit available from Technosoft Company. The theoretical finding is validated with experimental results that show the effectiveness of the real-time implementation.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Modeling of DC Motor and Choosing the Best Gains for PID Controllerijtsrd
NAMIKI 12 volt DC motor is used to model mathematically. L298 N hybrid motor driver is used to drive the motor. Voltage for the motor to drive to the desired angle is applied from the PWM of the motor. The best PWM value for the motor is considered by using PID controller from the Arduino processor. The actual angle of the motor is get from the optical encoders. The best gain for PID controller is get from Ziegler Nichols method and Simulink of MATHLAB software by using mathematical modelling equation of the motor. The modelling equation is get from two kinds of calculation. The first approach is from parameters estimation of the motor. The second is from first order differential equation. Then, the best PWM value is get for the position control of the motor. This motor is used for the robot arm. Ye Htet Aung | Tin Tin Hla "Modeling of DC Motor and Choosing the Best Gains for PID Controller" 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/ijtsrd26636.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26636/modeling-of-dc-motor-and-choosing-the-best-gains-for-pid-controller/ye-htet-aung
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Speed Control of PMSM by Sliding Mode Control and PI ControlIJMTST Journal
In order to optimize the speed-control performance of the permanent-magnet synchronous motor (PMSM)
system with different disturbances and uncertainties, a nonlinear speed-control algorithm for the PMSM servo
systems using sliding-mode control and disturbance compensation techniques is developed in this paper.
First, a sliding-mode control and PI control method based on one novel which allows chattering reduction on
control input while maintaining high tracking performance of the controller. Then, an PI control extended
sliding-mode disturbance observer is proposed to estimate lumped uncertainties directly, to compensate
strong disturbances and achieve high servo precisions. Simulation results PI control better than the SMC
control both show the validity of the proposed control approach.
The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
The application of the direct torque control strategy for induction machine drives is mainly characterized by torque and flux distortions caused by voltage vector limitation. The goal of this paper is to perform the conventional DTC induction machine drives and reduce ripples of both flux and torque response. The proposed contribution is based on the control of the DC output side of the rectifier feeding the voltage source inverter by means of PI controller in order to adapt the voltage vector used in typical DTC switching table. Mathematic models are built using MATLAB Simulink and programming environment; the simulation results show the difference between the proposed method and classical DTC.
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.
Investigating the Intelligent Methods of Loss Minimization in Induction MotorsTELKOMNIKA JOURNAL
Induction motors are widely used in industry. Given the increasing demand for electric machines in
different industries, optimization of these machines to achieve a high efficiency with low cost is of utmost
importance. Loss-minimization in motor is done in three ways: 1) optimizing motor selection and design; 2)
improving motor power supply waveforms; and 3) using appropriate controlling methods in drives. Often,
inductive motors provide the maximum efficiency in their nominal load. In most applications it is necessary for
a motor to work in light loads for a long time, e.g. in conveyors, elevators, etc. In these conditions, the
machine load is not the nominal load, and a higher percentage of the input power is lost. So, in the case of
variable load, the first and second methods cannot increase the efficiency; but the third method provides a
large flexibility in decreasing motor losses. In this paper, the application of the third method in lossminimization
is reviewed. These motor losses are mostly related to the controlling strategy and basically
occur in light-load conditions. There are various strategies to decrease this kind of losses, which are generally
divided into two categories: classic methods and intelligent methods. In this paper, first the classic methods,
including losses model control (LMC), flux control as a function of torque and search control (SC), are
discussed. Then the intelligent methods, such as genetic algorithm, PSO, fuzzy logic and artificial neural
network are investigated. This paper is presented while the last methods of efficiency improvement are being
investigated and each method is described briefly.
Designing the virtual model of a mechatronic micro positioning and micro-meas...eSAT Journals
Abstract This paper aims is to present the realization of a virtual model, as well as the experimental model for a mechatronic micro-positioning and micro-measuring system on two axes, OX, and OZ respectively. The paper also includes experimental results on measuring the gripping force developed between the fingers of an electrical micro-gripper, with two fingers, incorporated into the experimental model of a mechatronic micro-positioning system. Index Terms: flexible mechatronic system, micro-positioning, micro-gripper
Three phase induction motor Induction is one of the widest spread motor due to its
robustness, simple construction, no need for complex circuits for starting. With several
available speed control techniques, this paper presents a new Proportional-Integral (PI)
controller and Artificial Neural Network (ANNs) control system based on vector control
scheme. MATLAB/SIMULINK software may be used to create a 3phase induction engine
model. To achieve the effectiveness of the controller, the system is subjected to external
disturbance. Experimental results are presented and satisfied with the controller results.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
DSP-Based Sensorless Speed Control of a Permanent Magnet Synchronous Motor us...IJPEDS-IAES
In this paper, experimental results of 3-phase permanent magnet synchronous motor (PMSM) sensorless speed control are presented. To estimate the rotor position, a sliding mode current observer (SMCO) was implemented. This observer estimates the back emfs of the motor in the stationary reference frame using only the measured voltages and currents of the motor. These emfs were utilized to obtain the rotor position. The speed of the motor was calculated by differentiating the rotor position angle. The stability of the proposed SMCO was verified using Lyapunov method to determine the observer gain. The saturation function was adopted in order to reduce the chattering phenomenon caused by the SMCO. A vector control method was employed to achieve the sensorless drive system. The control application was developed in C/C++ language and implemented using the Texas Instruments TMS320LF2812 digital signal processor (DSP). This new processor enables intelligent control for motors. We used to test the drive the MCK2812 which is a professional development kit available from Technosoft Company. The theoretical finding is validated with experimental results that show the effectiveness of the real-time implementation.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Modeling of DC Motor and Choosing the Best Gains for PID Controllerijtsrd
NAMIKI 12 volt DC motor is used to model mathematically. L298 N hybrid motor driver is used to drive the motor. Voltage for the motor to drive to the desired angle is applied from the PWM of the motor. The best PWM value for the motor is considered by using PID controller from the Arduino processor. The actual angle of the motor is get from the optical encoders. The best gain for PID controller is get from Ziegler Nichols method and Simulink of MATHLAB software by using mathematical modelling equation of the motor. The modelling equation is get from two kinds of calculation. The first approach is from parameters estimation of the motor. The second is from first order differential equation. Then, the best PWM value is get for the position control of the motor. This motor is used for the robot arm. Ye Htet Aung | Tin Tin Hla "Modeling of DC Motor and Choosing the Best Gains for PID Controller" 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/ijtsrd26636.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26636/modeling-of-dc-motor-and-choosing-the-best-gains-for-pid-controller/ye-htet-aung
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Speed Control of PMSM by Sliding Mode Control and PI ControlIJMTST Journal
In order to optimize the speed-control performance of the permanent-magnet synchronous motor (PMSM)
system with different disturbances and uncertainties, a nonlinear speed-control algorithm for the PMSM servo
systems using sliding-mode control and disturbance compensation techniques is developed in this paper.
First, a sliding-mode control and PI control method based on one novel which allows chattering reduction on
control input while maintaining high tracking performance of the controller. Then, an PI control extended
sliding-mode disturbance observer is proposed to estimate lumped uncertainties directly, to compensate
strong disturbances and achieve high servo precisions. Simulation results PI control better than the SMC
control both show the validity of the proposed control approach.
The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
For Induction motor is a system that works at their speed, nevertheless there are applications at which the speed operations are needed. The control of range of speed of induction motor techniques is available. The robust control is used with induction motor and the performance of the system with the controller will be improved. The mathematical model to the controller, which were coded in MATLAB. The modeling and controller will be shown by the conditions of robustness of be less than one.
Speed Sensorless Vector Control of Unbalanced Three-Phase Induction Motor wit...IAES-IJPEDS
This paper presents a technique for speed sensorless Rotor Flux Oriented Control (RFOC) of 3-phase Induction Motor (IM) under open-phase fault (unbalanced or faulty IM). The presented RFOC strategy is based on rotational transformation. An adaptive sliding mode control system with an adaptive switching gain is proposed instead of the speed PI controller. Using an adaptive sliding mode control causes the proposed speed sensorless RFOC drive system to become insensitive to uncertainties such as load disturbances and parameter variations. Moreover, with adaptation of the sliding switching gain, calculation of the system uncertainties upper bound is not needed. Finally, simulation results have been presented to confirm the good performance of the proposed method.
Aplanning algorithm offive-axis feedrate interpolation based on drive and jer...IJRES Journal
CNC technology marks the core of modern manufacturing, and CNC interpolation module is one of the most important numerical control technology modules. Avery important feature of the CNC is to implement the feed rate that consists in producing the set points based on a NC program. In the high speed machining, the feed rate is restricted by the velocity, acceleration, and jerk. And the NURBS curve is a free curve, due to the many advantages of NURBS curves, it can be well applied to the CNC feed rate interpolation. The algorithm can get more smooth feed rate curves, which makes better use of kinematical characteristics of the machine. Finally, according to each machine axis capability, one can use the feed rate control method which is verified by simulation analysis and processing to test this method. The results show that the algorithm can effectively control the speed, acceleration and jerk.
Detection of Broken Bars in Three Phase Squirrel Cage Induction Motor using F...Dr.NAGARAJAN. S
Finite element method is more precise than the winding function approach, as it is based on the actual geometry of the machine and the machine model can easily be modified in order to study the effect of faults on the machine’s performance. Accurate models of the machine under healthy and faulty conditions are developed. This paper presents simulations of broken bars detection in a three phase squirrel cage induction motor under no load, half load and full load conditions for two and eight broken bars. The analysis is done using MagNet.
LIGHTWEIGHT MOBILE WEB SERVICE PROVISIONING FOR THE INTERNET OF THINGS MEDIATIONijujournal
Emerging sensor-embedded smartphones motivated the mobile Internet of Things research. With the
integrated embedded hardware and software sensor components, and mobile network technologies,
smartphones are capable of providing various environmental context information via embedded mobile
device-hosted Web services (MWS). MWS enhances the capability of various mobile sensing applications
such as mobile crowdsensing, real time mobile health monitoring, mobile social network in proximity and
so on. Although recent smartphones are quite capable in terms of mobile data transmission speed and
computation power, the frequent usage of high performance multi-core mobile CPU and the high speed
3G/4G mobile Internet data transmission will quickly drain the battery power of the mobile device.
Although numerous previous researchers have tried to overcome the resource intensive issues in mobile
embedded service provisioning domain, most of the efforts were constrained because of the underlying
resource intensive technologies. This paper presents a lightweight mobile Web service provisioning
framework for mobile sensing which utilises the protocols that were designed for constrained Internet of
Things environment. The prototype experimental results show that the proposed framework can provide
higher throughput and less resource consumption than the traditional mobile Web service frameworks.
SPEED AND TORQUE CONTROL OF AN INDUCTION MOTOR WITH ANN BASED DTCijics
Due to advantages such as fast dynamic response, simple and robust control structure, direct torque
control (DTC) is commonly used method in high performance control method for induction motors. Despite
mentioned advantages, there are some chronically disadvantages with this method like high torque and
current ripples, variable switching behaviour and control problems at low speed rates. On the other hand,
artificial neural network (ANN) based control algorithms are getting increasingly popular in recent years
due to their positive contribution to the system performance. The purpose of this paper is investigating of
the effects of ANN integrated DTC method on induction motor performance by numerical simulations. For
this purpose, two different ANN models have been designed, trained and implemented for the same DTC
model. The first ANN model was designed to select optimum inverter and the second model was designed to
use in the determination of the flux vector position. Matlab/Simulink model of the proposed ANN based
DTC method was created in order to compare with the conventional DTC and the proposed DTC methods.
The simulation studies proved that the induction motor torque ripples have been reduced remarkably with
the proposed method and this approach can be a good alternative to the conventional DTC method for
induction motor control.
Instrumentation and Automation of MechatronicIJERA Editor
This paper presents the methodology used for the automation of a mechanical system, which will be used to
perform scans on tooth surfaces, in this paper the mathematical modeling of the structure for further
implementation was carried out in order to get a reconfigurable device using specialized software. To carry out
this study various mathematical tools for developing the mathematical model were used, then control routines
that allow the manipulation mechanism for each axis independently performed. The implementation was carried
out by integrating various electrical, electronic and computer systems for an efficient control of the movement
and location of robot systems.
SIMULATION OF THE DRILLING PROCESS IN GFRP COMPOSITES USING SYSTEM DYNAMICS A...IAEME Publication
This paper intends to present the System Dynamics (SD) as a novel method to
simulate the thrust force developed during drilling of GFRP composites. Good quality
holes are extremely fundamental so as to accomplish equally good joints amid
creation of components prepared from composite for better execution. Since the
nature of a drilled hole is subject to material properties and machining conditions, it
is important to think about the impacts of these factors on the nature of hole obtained.
In the present work, the machining parameters thickness of the material, drill point
angle, drill size, drill speed and feed rate are selected to evaluate their effect on the
quality of the hole. Past works uncover the fact that the damage caused to the drilled
hole is primarily due to the thrust force. Consequently it is fundamental to limit the
thrust force so as to accomplish better quality of the drilled hole. The SD simulation
model was implemented through a causal loop diagram. A mathematical equation
used in the simulation was developed utilizing the Design of Experiments (DOE)
technique. VENSIM programming was utilized to create and run the SD model. The
SD simulation results were compared with Artificial Neural Networks (ANN) results,
Response Surface Methodoly (RSM) results and the experimental results. A decent
agreement was seen between SD, ANN and RSM results
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.
A Novel Technique for Tuning PI-controller in Switched Reluctance Motor Drive...IJECEIAES
This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.
Effect of Parametric Variations and Voltage Unbalance on Adaptive Speed Estim...IAES-IJPEDS
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Fuzzy controller and neural estimator
1. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
DOI : 10.5121/ijaia.2015.6402 17
FUZZY CONTROLLER AND NEURAL ESTIMATOR
APPLIED TO CONTROL A SYSTEM POWERED BY
THREE-PHASE INDUCTION MOTOR
Élida Fernanda Xavier Júlio1
, Simplício Arnaud da Silva2
, Cícero da Rocha Souto3
and Isaac Soares de Freitas4
1
Postgraduate Program in Mechanical Engineering, Federal University of Paraíba, João
Pessoa, PB, Brazil
2
Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, PB,
Brazil
3
Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, PB,
Brazil
4
Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, PB,
Brazil
ABSTRACT
In this study, a control strategy is presented to control the position and the feed rate of a table of a milling
machine powered by three-phase induction motor, when machining pieces constituted by different types of
materials: steel, brass and nylon. For development of the control strategy, the vector control technique was
applied to drive the three-phase induction machines. The estimation of the electromagnetic torque of the
motor was used to determine the machining feed rate for each type of material. The speed control was
developed using fuzzy logic Takagi-Sugeno (TS) model and the estimation of the electromagnetic torque
using the artificial neural network (ANN) of the least mean square (LMS) algorithm type. The induction
motor was fed by a three-phase voltage inverter hardware driven by a digital signal processor (DSP).
Experimental results are presented.
KEYWORDS
Position Control, Feed Rate Control, Estimated Electromagnetic Torque, Fuzzy Logic, Artificial Neural
Networks
1. INTRODUCTION
Milling is a machining process which consists of removing material from a piece, in order to
construct flat surfaces or with a certain form. The removal of material is performed intermittently
by the combination of two movements performed simultaneously: the rotation of the cutter
around its axis and the linear movement of the milling machine table where the piece to be
machined is fixed [1].
In many of machining systems, constant values of feed rate and cutting speed are established
throughout the tool path in the machining of surfaces, which can be very expensive for
manufacturers [2].
2. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
18
Cutting parameters for machining should be monitored and adjusted automatically by selecting
them appropriately to the machining process, especially the parameters of feed rate and cutting
speed [3, 4]. In the work [5], an algorithm to adjust the feed rate automatically was developed
with the goal of achieving maximum productivity in machining of a manufacturing line.
Difficulties as the geometric complexity of pieces, high hardness and roughness of materials are
identified in the machining of free and complex formats of surfaces. In these cases, the best
method to control the occurrence of impacts is to regulate and control the cutting parameters
according to the shape and the surface structure.
One of the effective ways to improve CNC machining efficiency is to use optimal cutting
parameters. An optimization method of cutting parameters for machining free formats of surfaces
was developed by applying the adaptive control of the feed rate in [6].
Due to the need of machining systems that would provide drives with variable speed, milling
machines have increasingly been driven by three-phase induction motors. Such motors are widely
used due to their low cost, ability to operate with a variety of loads in adverse conditions,
simplicity of construction and maintenance.
In three-phase induction motor, the implementation of vector control for direct field orientation
allows that the position of the flux is determined by measuring the magnitudes of stator terminals:
voltage and current [7].
Control strategies for milling can be developed through programming algorithms with application
of intelligent controllers and estimators, using fuzzy logic and artificial neural network. The fuzzy
and ANN techniques deal with nonlinearities easily, enable the control of complex multivariable
systems and dispense mathematical modeling of the processes.
A methodology using ANN associated to fuzzy logic was presented in thesis [8] for construction
of a machining process controller, because of analytical complexity and non-linear responses of
this machining system.
Fuzzy logic enables the implementation of human experience in systems. The Takagi-Sugeno
fuzzy model is able to represent, approximate or exact shape, any nonlinear dynamics as a
combination of locally valid linear models, by interpolating smoothly [9]. The TS fuzzy technique
combines a fuzzy rule-based method and a mathematical method, using conditional propositions,
whose antecedents and consequents are linguistic variables and linear equations, respectively
[10].
A fuzzy control strategy for end milling process was presented in [11]. In this work, the fuzzy
controller was implemented adaptively aiming to maximize the feed rate for a slow machining of
complex shapes surfaces.
In the research [12], a fuzzy approach was developed to determining the optimum feed rate for
the geometric features of a piece to be milled.
The artificial neural network is a technique organized according to human neural structure, which
acquires knowledge through a learning process, with parallel and adaptive processing [13].
The LMS learning algorithm is an ANN of error minimizing, based on instant estimations of error
in the output [14]. In the first work of great relevance applying LMS algorithm [15], the
interference cancellation using adaptive filters was an important reference in the field of signal
digital processing.
3. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
19
The LMS algorithm technique was used to develop an adaptive filter for reducing and eliminating
noise of interference signals in frequency radio at work [16], due to the simplicity of
implementation and low computational complexity of this algorithm.
In reference [17], estimation of the electromagnetic torque of a three-phase induction motor was
obtained by applying the LMS algorithm. For this, neural adaptive filters were developed to
eliminate offsets in the estimation of the stator flux.
The machining system developed for the realization of this work has a vertical milling machine.
The table of the milling machine is composed of two bases, one called X base and the other Y
base, powered by three-phase induction motors. In this work, the drive and control of the X base
was performed in the machining process of materials: steel, brass and nylon.
The objectives of this work are: trigger the system with specific feed rates for cutting each
material; control the position and the feed rate of the X base of the milling machine table, in the
machining process of pieces constituted by different types of materials, called specimens. The
rotation of the cutting tool is constant in this process.
A speed controller using the TS fuzzy model is developed to control the feed rate of the X base.
The estimation of the electromagnetic torque of the motor of this base is performed using ANN of
the LMS algorithm type. A DSP is programmed to implement the control strategy of the
machining system.
2. DEVELOPMENT OF THE SYSTEM
The vertical milling machine of machining system is shown in Figure 1 whose cutting tool is an
end mill. The X base (upper base) of the milling machine table has a course of 200 mm. On this
base, the specimens that were submitted to the frontal machining processes were fixed.
Figure 1. Vertical milling machine
4. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
20
Two types of specimens were prepared, i.e., a specimen constituted of steel and brass, and the
other constituted of steel and nylon.
The machining of a specimen generates the imposition of load to the induction motor of the X
base. Therefore, the electromagnetic torque of this motor is estimated, in order that through this
estimation be verified the type of material machined. Thus, the specific feed rates are applied to
machining each type of material of a specimen from the signal of the estimated electromagnetic
torque.
As the pitch of the X base trapezoidal spindle is 4 mm and a complete revolution of the motor
shaft corresponds to 2π rad, a numerical factor of 0.032 mm/rad was obtained. The position of X
base is determined from multiplying the angular position of rotor of the motor by 0.032 mm/rad;
as well as, the feed rate of this base is obtained by multiplying the rotational speed of the rotor by
this numerical factor.
In Figure 2, the system configuration for the drive and control of the X base of the milling
machine table is schematized. In this diagram, are presented: the digital signal processor, used in
the processing, transmission and data acquisition; the hardware constituted by three-phase voltage
inverter, which feeds the three-phase induction motor of the X base; the encoder for measuring
the angular position and rotational speed of rotor of the motor, thereby obtaining the position and
feed rate of the X base; and Hall effect sensors, used to obtain the currents and voltages of the
motor stator.
Besides the electrical and electronic components, in Figure 2, are represented: the control system,
developed for drive and control the X base of the milling machine, and the estimation system of
the electromagnetic torque of the motor of this base.
Figure 2. Schematic diagram for control the X base of the milling machine
2.1. Control System
The control system of the milling machine was developed in closed loop, controlling the three-
phase induction motor of the X base. For this, a current controller using a proportional-integral
(PI) controller, and a speed controller using a TS fuzzy model were developed.
5. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
21
Figure 3. Speed fuzzy controller
The input variables Error and Derror are defined in the fuzzification step. Error is the difference
between the reference value and the value of the rotational speed of rotor ωr, and Derror is the
derivative of this error. The universes of discourse of Error and Derror comprise a normalized
range of -1 to 1.
Each variable, Error and Derror, consists of seven pertinence functions with triangular and
trapezoidal shapes, called: negative big (NB), negative medium (NM), negative small (NS),
almost zero (AZ), positive small (PS), positive medium (PM) and positive big (PB). The
arrangements of linguistic terms of the Error and Derror are presented in Figure 4 and Figure 5,
respectively, in their universes of discourse.
Figure 4. Pertinence functions of the input variable Error
Figure 5. Pertinence functions of the input variable Derror
In fuzzy inference step, the forty-nine control rules developed are inserted in Table 1. For the
composition of each rule and the relationship between them, it was applied the max-min inference
technique. So, to model each sentence was used min and the relationships between rules were
modeled by applying max.
6. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
22
Table 1. Table of fuzzy rules.
Derror
Error
NB NM NS AZ PS PM PB
NB iNB iNB iNB iNB iPM iPB iPB
NM iNB iNB iNB iNM iPS iAZ iPB
NS iNB iNB iNM iNS iAZ iPS iPM
AZ iPB iPM iPS iAZ iNS iNM iNB
PS iNM iNS iAZ iPS iPM iPB iPB
PM iNB iAZ iNS iPM iPB iPB iPB
PB iNB iNB iNM iPB iPB iPB iPB
In Figure 3, it can be observed that PD fuzzy generates the its output variable in the stage of
defuzzification. At this stage, a linear and time-invariant model is determined using Takagi-
Sugeno fuzzy method [18].
The its variable is obtained by a weighted average in Eq. (1), in which the terms itsx, itsy and itsz are
expressed by Eq. (2), Eq. (3) and Eq. (4), respectively. This equation consists of linear functions
defined from the consequents of the control rules and of the numerical values of the input
variables Error and Derror.
iPSiPMiPBiAZiNSiNMiNB
iii
i
tsztsytsx
ts
++++++
++
= (1)
)5.01.0()3.02.0()1.05.0( DerrorErroriNSDerrorErroriNMDerrorErroriNBitsx ×−×+×−×+×−×= (2)
)0.12.0( DerrorErroriAZitsy ×−×= (3)
)5.01.0()3.02.0()1.05.0( DerrorErroriPSDerrorErroriPMDerrorErroriPBitsz ×−×+×−×+×−×= (4)
From the complex model of representation of a three-phase induction machine, the current
controller was developed applying the control quadrature with referential in rotor flux (b).
'
( )
b br
s s
l
I V
s
σ
η
=
+
(5)
2
1
s r ml l l
σ =
−
(6)
2
m
r s
r
l
l R
σ
η σ
τ
= + (7)
7. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
23
1
4ip
v r
k
T l σ
= (8)
ii pi kk η= (9)
2.2. Estimation System
The complex model of representation of the three-phase induction machine was applied for the
development of the estimation project of the electromagnetic torque of the three-phase induction
motor, using control quadrature with fixed reference in the stator (a) and applying an ANN of the
LMS algorithm type.
Initially, for the estimation of the electromagnetic torque, it was estimated the stator flux of the
three-phase induction motor.
( )∫ −= dtiRv a
s
a
sd
a
sd sdλ (10)
( )∫ −= dtiRv a
s
a
sq
a
sq sqλ (11)
Due to the occurrence of continuous current levels, called offset, in measuring voltages and
currents of the motor, caused by the analog components and by the amplifier circuits constituting
the voltage and current sensors, offset arose in sign of the counter-electromotive force.
To the elimination of offset in this signal, it was developed a neural adaptive filter, by the
technique of LMS algorithm. A neural structure was implemented for each component of the
counter-electromotive force, d and q, similarly.
8. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
24
Figure 6. LMS adaptive filtering of the counter-electromotive force
Figure 7. LMS adaptive filtering of the estimated stator flux
)(2 nya
sd
a
sdf −= λλ (12)
a
sdfnyny µλ2)()1( 22 +=+ (13)
In Eq. (13), the learning rates µ of 0.0001, 0.0005 and 0.001 were used due to the specific feed
rates applied for the machinings of steel, brass and nylon, respectively.
After the estimation of the stator flux and the elimination of offsets, it was estimated the
electromagnetic torque of the motor of the milling machine using Eq. (14). In this equation, the
estimated electromagnetic torque ceest was determined using the estimated stator flux filtered, the
stator current and the constant of the number of pole pairs P of the motor, which is equal to two.
( )a
sqf
a
sd
a
sdf
a
sqest iiPce λλ −=
(14)
3. EXPERIMENTAL RESULTS
To perform the machining of specimens of steel/brass and steel/nylon, the X base was driven with
reference signals of position step type of positive and negative amplitudes, resulting in
displacements of this base in right and left directions, respectively, with referential in front of
milling machine.
9. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
25
From the signal of estimation of the electromagnetic torque of the motor of the X base in the
machining of materials of the specimens, this base was driven with references of specific feed
rates.
These references of speed were set for the operational conditions of machining of materials in the
milling machine, with a rotation of the cutting tool of 1500 rpm, at a cutting depth of 2 mm in
relation to the specimen surface, and work penetration of 6.35 mm, which corresponds to the
diameter of the milling cutter. Therefore, for machinings of steel, brass and nylon, the feed rate
references set were, in module, 1.6 mm/s, 5.6 mm/s and 8.0 mm/s, respectively.
In Table 2, the average values of estimated electromagnetic torques ceest and the respective feed
rate references v* of the X base are presented.
In this table, in steel machining, the average values of torque ceest of 0.42 Nm and of -0.17 Nm
were verified when driving the X base to the right and to the left, respectively.
Table 2. Estimated electromagnetic torques and reference speeds.
Displacement direction Material ceest (Nm) v* (mm/s)
right steel 0.42 1.6
right brass 0.90 5.6
left steel -0.17 -1.6
left nylon -0.22 -8.0
At the initial instant operating of the system, the specimens were positioned 4 mm to 6 mm away
from the milling cutter. The system was driven on empty since the departure of the system until
the specimen reaches the milling cutter.
3.1. First Experiment
Initially, the curves of response and of reference of the position variable of the X base are
presented in Figure 8. In this test, the X base was driven with a reference signal of step type with
amplitude of 77 mm, performing the machining of the steel/brass specimen. By analyzing the
response curve obtained, there was a settling time of 32.36 s, a steady-state error of 0.12 % and
non-occurrence of overshoot.
0 5 10 15 20 25 30 35 40 45
0
10
20
30
40
50
60
70
80
Time (s)
S,S
*
(mm)
S
S*
Figure 8. Response and reference curves of the position of X base
10. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
26
Then, in Figure 9, the reference curve of feed rate of the X base and the response curve obtained
are presented. As the system functioned initially empty, to drive the X base was applied a signal
of the type speed ramp with amplitude of 1.28 mm/s, keeping constant speed until the instant of
8.64 s.
From this instant, due to the estimation of the electromagnetic torque obtained in steel machining,
a speed ramp with amplitude of 1.6 mm/s was observed at an interval of 0.1 s and remained
constant until 25.29 s. At that instant, a speed ramp with amplitude of 5.6 mm/s was verified at an
interval of 0.1 s, due to the estimated electromagnetic torque obtained in the brass machining, and
the speed remained constant until a driving v* by null value, resulting thereby in the braking of
the X base. Based on the analysis of Figure 9, there were null steady-state errors, in the time
intervals in which speed references were constants, and non-occurrence of overshoots.
0 5 10 15 20 25 30 35 40 45
-1
0
1
2
3
4
5
6
Time (s)
v,v
*
(mm/s)
v
v
*
Figure 9. Response and reference curves of the feed rate of X base
For analysis of neural estimation of the electromagnetic torque of the three-phase induction motor
of the X base, in Figure 10, a curve of the estimated electromagnetic torque ceest obtained in the
machining of the steel/brass specimen was observed. In this graph, it was verified an average
value of torque ceest of 0.42 Nm in steel machining, in the range of 8.64 s to 25.29 s, and an
average value of ceest of 0.90 Nm in the brass machining, in the range of 25.29 s to 32.49 s.
5 10 15 20 25 30 35 40 45
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Time (s)
ceest
(Nm)
ceest
Figure 10. Curve of the estimated electromagnetic torque
11. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
27
3.2. Second Experiment
For machining the steel/nylon specimen, the X base was driven with a reference signal of step
type with amplitude of -46 mm, as shown in the response and reference curves of the position of
X base in Figure 11. In this graph, there was a settling time of 24.76 s, a steady-state error of 0.20
% and non-occurrence of overshoot.
0 5 10 15 20 25 30 35
-50
-40
-30
-20
-10
0
Time (s)
S,S
*
(mm)
S
S*
Figure 11. Response and reference curves of the position of X base
In Figure 12, the reference curve of the feed rate of X base and the response curve obtained are
presented. Initially, it was observed a signal of the type speed ramp with amplitude of -1.28
mm/s, keeping this speed constant. At the instant of 8.68 s, due to the torque ceest obtained in the
steel machining, there was a speed ramp with amplitude of -1.6 mm/s, which remained constant
until 23.17 s. From this instant, it was verified a speed ramp with amplitude of -8.0 mm/s, due to
the torque ceest obtained in the nylon machining, and this amplitude remained constant until
reaching the desired position. By the analysis of Figure 12, there were null steady-state errors, in
the time intervals in which speed references were constants, and non-occurrence of overshoots.
0 5 10 15 20 25 30 35
-10
-8
-6
-4
-2
0
2
Time (s)
v,v
*
(mm/s)
v
v
*
Figure 12. Response and reference curves of the feed rate of X base
In Figure 13, the curve of the estimated electromagnetic torque of the motor of X base is
presented in the machining of the steel/nylon specimen. In this graph, it was observed an average
value of ceest of -0.17 Nm in steel machining, in the range of 8.68 s to 23.17 s, and an average
value of ceest of -0.22 Nm in the nylon machining, in the range of 23.17 s to 24.78 s.
12. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
28
5 10 15 20 25 30 35
-0.25
-0.2
-0.15
-0.1
-0.05
0
Time (s)
ceest
(Nm)
ceest
Figure 13. Curve of the estimated electromagnetic torque
4. CONCLUSIONS
In this work, the controls of position and of feed rate of a milling machine were presented
applying, automatically, specific feed rates when machining the materials.
Through the response curves of the two experiments, for position, it was verified a maximum
steady-state error of 0.20 %, with no overshoots in any of the machining processes. By observing
the feed rate curves, the fuzzy controller provided the obtaining of null steady-state errors in both
experiments, in the intervals driving with constant speeds, not occurring overshoots.
The modeling of the speed controller by Takagi-Sugeno fuzzy technique made possible the feed
rate control not only in the machining of hard materials, such as steel and brass, but also in the
machining of soft material, such as nylon, controlling this speed in permanent and transient
regimes, when changing from one type of material to another.
The applications of the neural technique of LMS algorithm in the estimations of the stator fluxes
possibilited estimate the electromagnetic torques of the motor of the milling machine simply and
effectively. In the estimations of torque, the convergence of the two ceest signals was observed.
Through the performances of machining processes carried out, it was verified the functionality
and effectiveness of the developed control strategy. Once, from the estimation of the
electromagnetic torque of the milling machine motor in the machining of the specimen materials,
it was possible to machine each material with specific feed rate for its cut.
As the results obtained were coherent, presenting the expected performances, it is concluded that
the control strategy developed for milling machine of this work was very effective in the
machining of different types of materials in the same process.
A perspective for future work is to develop a strategy for the controls of cutting speed and of feed
rate of a machining system, allowing machining, continuously, a piece constituted by different
types of materials.
13. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 4, July 2015
29
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