Speed Control Of Induction Motor Research Articles

Sensorless speed controller of an induction motor with MRAS-based model predictive control

Speed sensors play critical role in induction motor (IM) control systems. However, the use of these sensors cause several drawbacks such as increasing the overall cost, decreased reliability on control and increased noise problems in data acquisition. The sensorless control methods also have been proposed in control systems of IM to increase the overall performance. On the other hand, model predictive controllers (MPCs) which is one of the most recently used controller methods require the speed measurement to perform speed and torque control of an IM. In addition to control method, the topology of voltage source inverter (VSI) affects the overall efficiency of the IM drive because of its losses and total harmonic distortion (THD) ratios delivered by the topology. The main proposal of this study is to implement an IM drive built with a three-level active neutral point clamped (3L-ANPC) voltage source inverter (VSI) and to propose an MPC that eliminates sensor requirement in speed control of IM. The proposed IM drive takes benefits of multilevel inverter topology, which reduces the torque ripple, enables low switching frequency and provides decreased harmonic distortion. The switching pulses of ANPC VSI are generated by the proposed sensorless MPC method for controlling the torque of IM. The modelling and experimental verification of the ANPC based IM drive have been carried out and the sensorless MPC controller has been validated in this study. There two types of model reference adaptive system (MRAS) have been proposed to ensure precise estimation of IM speed that should be supplied to MPC controller. These MRAS methods are titled as the rotor flux based model reference adaptive system (MRASF), and the stator current based model reference adaptive system (MRASCC) according to estimation references. The proposed MPC-based IM drive has been analyzed under variable speed and torque circumstances to detect the efficiency and reliability of MRASF and MRASCC algorithms. The efficiency indicators such as flux and torque responses, and speed control of the suggested MPC IM drive are validated with experimental analysis. The results of revealed that the IM effectively tracks the reference speed and produces the required torque depending to the precise control of proposed MPC algorithm.

Speed Control of a Linear Induction Motor Using V/F Technique

Speed control of single phase Linear Induction Motor using V/F method have been widely used in fans and pumps drives. High-power Linear Induction Motors (LIMs) face challenges in managing speed. Traditional system are ineffective, causing inefficiency and potential damage. To address this, our project proposes a control system for LIM speed control suing V/f method. Currently, linear induction motors (LIMs) are widely used in many industrial applications, including transportation, conveyor systems, actuators, material handling, pumping of liquid metal, sliding-door closers and others, with satisfactory performance. The proposed control system consists of Arduino controller , soft switching for controlling and motor driver board to run Linear induction motor with voltage and frequency monitoring. This control system has almost no effect in normal operation of LIM. Hardware testing results are obtained to verify the valuable operation of proposed circuit in Speed control of LIM. Keywords—Linear Inuction Motor , Speed Control, V/f Method , Arduino Controller , LCD Display etc.

Speed Control of Induction Motors using Neuro Fuzzy Expert Systems

Induction motors and their speed control is critical aspect in several real life applications. Often, the proportional integral derivative (PID) controlling mechanism is found in several applications in industries where speed of motors or induction motors is to be controlled. The PID controller is specifically useful since it tries to minimize the steady state error as well as increase the response or speed of the system, thereby incorporating the benefits of proportional derivative and proportional integral control. However, the real time operation of PID controllers is challenging due to its tuning. The controlling mechanism is critically important for the application of the PID. Previously, manual tuning was used which needed experts and was also prone to errors. With the advent of sophisticated optimization tools, automatic tuning has gained momentum. In this work, a combination of neural networks and fuzzy logic often called neuro fuzzy expert systems has been proposed for the speed control of induction motors. It has been shown that proposed system is capable to attain better results compared to conventional techniques. The system has been designed on Matlab/Simulink. Key Words: Induction Motors, Speed Control, PID Controllers, Fuzzy Logic, Neuro Fuzzy Expert Systems.

Robustness Enhancement of Fractionalized Order Proportional Integral Controller for Speed Control of Indirect Field-Oriented Control Induction Motor

Robustness Enhancement of Fractionalized Order Proportional Integral Controller for Speed Control of Indirect Field-Oriented Control Induction Motor

Speed Control of Induction Motor using Fuzzy Logic

Abstract: In this paper, speed control of induction motor using fuzzy logic controller is proposed. Speed control of induction motor takes place by, Direct torque control(DTC) method i.e. by directly controlling torque. Here we have used Voltage/Frequency speed control method of induction motor. The fuzzy logic controller (FLC) solves the problem of non linearity’s and parameter variation of induction motor. Unlike the conventional standard controllers, the proposed controller has much less computationally demanding. Direct torque control scheme of induction motor is firstly used. Then, the specified rule and their membership functions of proposed fuzzy logic system will be represented. The performance of a controller is evaluated under various operating conditions. A simplified FLC with relatively fewer rules will be implemented for perfect speed control.

An Assessment and Performance of Voltage Source Inverter Feed Induction Motor Drive

Abstract: This paper conclude speed control of induction motor the induction motor are use in the industrial drives because they are self starting, reliable and economical they use in the fix and variable speed. The voltage and frequency are varies speed are also varies. This present performance and evaluation of voltage source inverter feed induction motor drive. By varying stator terminal voltage from voltage source inverter. The model are PWM system use to vary voltage and frequency. PWM-Inverter fed 3-phase Induction Motor, and the Torque was found to be constant for various rotor speeds. PWM-Inverter fed 3- phase Induction Motor. It was observed that using a Closed-Loop scheme with a Proportional Controller gave a very superior way of controlling the speed of an Induction motor while maintaining a constant maximum torque. Simulation results are obtained using MATLAB/SIMULINK environment for the effectiveness of the study.

Study and Evaluation a New Predictive Control Method for Speed and Stator Current Control of Induction Motor

It is clear that modern industries rely heavily on electric motors, especially induction motors. These motors convert electrical energy into mechanical energy. The distinction in the performance of the induction motor lies in that it is powerful when exposed to various operational and environmental variables, and it is also inexpensive. However, there are many traditional disadvantages that appear during the operation of the induction motor in its non-linear mechanical properties in addition to the difficulty in regulating the speed of the motor. In this paper , we present a new control method for controlling the stator current and speed of induction motor based on current control with speed control technique. The present model is based on conventional predictive controller development with a structure which is similar to rotor control and the direct torque control .It has double loops and both loops will use the prediction power. The inner loop controls the stator current based on Finite Control Set - Model Predictive Control (FCS-MPC) and the outer loop controls the speed to maximize the dynamic response of the loop. The MATLAB software has been used to implement the controller circuit. The obtained simulation results indicates that the presented control method has comparable performance to conventional controllers with some reduction in the overshoot and fast response interval.

Evaluation of intelligent PPI controller for the performance enhancement of speed control of induction motor

Induction motor control for high dynamic performance industrial applications is often difficult due to parameters sensitivity and its non-linearity. Through independent manipulation of the flux and torque producing components, Field Oriented Control (FOC) methodologies have been used to improve dynamic performance, widen speed range, and torque/flux quality. Studies have shown that the Parallel Proportional Integral (PPI) compensation method produces responses of higher quality than the traditional cascaded Proportional Integral (PI) type though tuning gains for PPI controller is time consuming and inaccurate. In this research work, modeling of control strategy for Induction motor and tuning of four parameters (Ki, Ki1, Kp, Kp1) of PPI controller at the outer speed control loop have been conducted using GA and PSO algorithms. The speed and torque responses' performances of the proposed controller, PPI-PSO and PPI-GA, have been contrasted with each other. For simulation, SIMULINK and a programming environment of a MATLAB software were employed. The simulation result illustrated that PSO based tuning out performs the GA based tuning in terms of dynamic speed response, torque quality and disturbance rejection capability in the newly control mechanism. Although the speed trajectory tracking performance of both the controllers presented in this study is comparable, comparatively low dynamic performance was found for PPI-GA controller at both high and low speed tests inside the same search space. The PPI-PSO controller yields an optimal result having 0.270 % overshoot, a settling time of 8.185 s, and a rise time of 8.037 s in comparison with PPI-GA controller which exhibits overshoot of 0.640 %, a settling time of 9.596 s, and a rise time of 8.615 s with high-speed trajectory tracking test.

Fractional Order Weighted Mixed Sensitivity-Based Robust Controller Design and Application for a Nonlinear System

This paper focuses on fractional-order modeling and the design of a robust speed controller for a nonlinear system. An induction motor (IM), widely used in Electrical Vehicles (EVs), is preferred in this study as a well-known nonlinear system. The major challenge in designing a robust speed controller for IM is the insufficiency of the machine model due to inherent machine dynamics. Fractional calculus is employed to model the IM using the small-signal method, accounting for model uncertainties. In this context, experimental data is approximated using a fractional-order small-signal transfer function. Consequently, a mixed sensitivity problem is formulated with fractional-order weighting functions. The primary advantage of these weighting functions is their greater flexibility in solving the mixed sensitivity problem by involving more coefficients. Hereby, three robust speed controllers are designed using the PID toolkit of the Matlab program and solving the H∞ mixed sensitivity problem, respectively. The novelty and contribution of the proposed method lie in maintaining the closed-loop response within a secure margin determined by fractional weighting functions while addressing the controller design. After evaluating the robust speed controllers with Bode diagrams, it is proven that all the designed controllers meet the desired nominal performance and robustness criteria. Subsequently, real-time implementations of the designed controllers are performed using the dsPIC microcontroller unit. Experimental results confirm that the designed H∞-based fractional-order proportional-integral-derivative (FOPID) controller performs well in terms of tracking dynamics, exhibits robustness against load disturbances, and effectively suppresses sensor noise compared to the robust PID and fixed-structured H∞ controller.

IMPLEMENTASI DIMMER AC BERBASIS ARDUINO PADA PENGATURAN KECEPATAN MOTOR INDUKSI SATU FASA

Some things that must be considered to improve the work of an induction motor are the rotational speed of the motor which is expected to continue to be increased, torque that can meet the load, and higher efficiency. Multilevel inverters will play an important role in converting DC energy from battery sources into AC electrical energy. This study aims to create a single-phase induction motor speed control system with an Arduino Mega-based Multilevel inverter. External input in this study uses a normally close push button with two initializations, namely the Up and Down push buttons. The push button functions as an induction motor speed control with speed conversion in the form of a percent parameter. Parameters will appear on the LCD display for every change that occurs. The test results show that the induction motor will start rotating at a percentage of 20%, namely at a speed of 1631.8 RPM, then Up to 40% at a speed of 2260.2, Up again to 60% at a speed 3261.8 RPM, and at 80% speed it produces 4163.8 and the highest speed reaches 5241.4 RPM with a speed percentage of 100%. The rotational speed of the motor can be displayed on the LCD display in the form of a digital display.

Adaptive Linear Quadratic Gaussian Speed Control of Induction Motor Using Fuzzy Logic

Adaptive Linear Quadratic Gaussian Speed Control of Induction Motor Using Fuzzy Logic

Speed Control of Three Phase Induction Motor using PI Controller with Space Vector Width Modulation (SVPWM) Technique

This article aimed to design and simulated the speed control of a three-phase induction motor using a PI controller with Space Vector Pulse width modulation technique. The induction motor used in this article is was designed at the Electrical Energy Conversion Laboratory, Riau University, with a power of 1.1 kW, 380 V, 2-pole. Meanwhile, the PI controller constants used in designing this induction motor were determined using the Fine Tunning method to obtain KP and KI values of 3.539 and 9.526, respectively. The tests were carried out by running simulations in three conditions, namely no load, full load, and variable load at a speed of 2800 rpm. The test results showed that the use of a PI controller can improve the speed response of induction motors by eliminating the steady state error. This is in addition to increasing the rise time response of the motor speed by 0.012s and 0.046s at no load and full load, respectively, when the rise time analysis is at the same value. It can also accelerate the motor to reach a peak speed of 0.247 s and 0.166s at no load and full load. In addition, SPVWM with PI controller can maintain speed setting even though there is a load change during operation, which can be verified with load testing.

Simulation and Implementation of Speed Control of Single Phase Induction Motor Using Microcontroller

Simulation and Implementation of Speed Control of Single Phase Induction Motor Using Microcontroller

Main Circuit Parameter Stability Region of Induction Motor Speed Control System Based on BBMC

Asynchronous motor speed control system based on Buck-Boost matrix converter ( BBMC ) has good speed regulation performance. Because the inverter stage of BBMC is a variable structure and nonlinear system, BBMC will produce bifurcation and chaos with the change of parameters, resulting in an unstable state of the system. The main factors affecting the stability of BBMC are the main circuit and control parameters. The control parameters can be continuously optimized in the later stage, and the main circuit parameters cannot be changed once they are determined. This paper studies the stability region of main circuit parameters, and the discrete iterative mapping model of the asynchronous motor speed control system based on BBMC is established. According to the sampling distribution of BBMC output voltage, the influence of main circuit parameters inductance L and capacitance C on the stability of the speed control system is analyzed. The stability region of the main circuit parameters is determined by numerical fitting. Finally, simulation and experiment verify the above analysis’s correctness. The stability region of main circuit parameters can provide the basis for selecting main circuit parameters in practical engineering.

Dynamic performance improvement of induction motors used in rolling mill application: A two-loop model predictive control strategy

In this paper, a novel two-loop model predictive control (TLMPC) is proposed to enhance the dynamic performance of induction motors used in rolling mill applications. In such applications, two individual voltage source inverters feed induction motors which are connected to the grid in a back-to-back manner. The grid-side converter, responsible for controlling the DC-link voltage, plays a vital role in the dynamic performance of the induction motors. Its undesired performance deteriorates the speed control of induction motors, which is a crucial need in the rolling mill industry. The proposed TLMPC includes a short-horizon finite set model predictive control in the inner loop to control the power flow by finding the best switching state of the grid-side converter. Additionally, a long-horizon continuous set model predictive control is developed in the outer loop for adjusting the set point value of the inner loop by predicting the DC-link voltage in a limited time horizon. An identification approach is exploited to approximate the non-linear model of the grid-side converter in order to use it in the outer loop. The mathematical proof of the robust stability of the proposed TLMPC is provided and its real-time execution is also certified. Finally, the capability of the proposed approach is evaluated using MATLAB/Simulink. A sensitivity analysis to evaluate the effect of the model’s inaccuracy and uncertainties on the performance of the proposed strategy is also provided.

Adaptive Fuzzy-PI for Induction Motor Speed Control

Adaptive Fuzzy-PI for Induction Motor Speed Control

Speed Control of Induction Motor by Using Voice Recognition Using Bluetooth

The project's goal is to develop a voice recognition and Bluetooth communication-based system for managing an induction motor's speed. The system is made up of two primary components: the speech recognition module, which is in charge of understanding user voice instructions, and the motor control module, which regulates to response to user voice commands. A speech recognition algorithm processes the user's vocal commands after being captured by the voice recognition module's microphone. control module receives the commands from the algorithm via Bluetooth communication. PWM method is used by the module to adjust the speed of induction after receiving commands. Without the use of physical buttons or switches, voice recognition technology offers customers a simple and practical way to adjust the motor's speed. The study shows that voice recognition technology and Bluetooth connectivity may be used to regulate the speed of induction motors, providing a practical and user-friendly solution for a variety of applications. Key Word: Induction motor, voice recognition, pulse-width modulation.

Reactive power model reference adaptive speed-sensorless system with direct torque control tuned with fuzzy neural networks for improved speed control in induction motor drives

This article investigates a reactive power based Model Reference Adaptive System (Q-MRAS) with Fuzzy Neural Network (FNN) facilitated Direct Torque Control for improved speed control of an Induction Motor Drive. A key component of the conventional DTC control scheme is the use of PID controllers and its derivatives. PID controllers were found to be a source of ripples. To mitigate its effect, in this work, a novel idea of replacing all the PI controls in the conventional speed control DTC models with FNN based controllers is investigated. The goal is to reduce complexity, reduce ripples in speed and boost low speed operation suitable for industrial needs. The proposed FNN controllers-based model is implemented using Matlab/Simulink. Comparisons are made between the results obtained from the proposed model and that from conventional models (Switching Table based DTC and SVM-DTC). Results showed that in the proposed FNN based DTC model, low speed operation (100 rpm) had 0.13 % speed ripple compared to 0.94 % in the conventional Switching Table based DTC and 0.23 % in the conventional SVM-DTC. This represents a reduction in speed ripple by 86.17 % in the proposed scheme compared to the Switching-Table DTC and by 43.48 % in the proposed scheme compared to the SVM-DTC scheme.

Adaptive Fuzzy-PI for Induction Motor Speed Control

An induction motor (IM) is one type of AC motor which widely used. IM is chosen due to its simplicity, reliability, efficiency, and low cost. There are many methods proposed to control the speed of IM which is known as variable speed drive (VFD). In this study, the DTC method is used since it is more robust to the parameter’s changes. The combination of the Fuzzy and PI method is used in speed control. PID performances decrease when the system condition changes. Therefore, fuzzy is used as an adaptive algorithm to vary the PID gain. It was superior in terms of settling time, overshoot/ undershoot, and IAE compared to the PI method. It has lower IAE in both speed tracking and loaded conditions by 44.98% and 4.47%, respectively.

Implementation of a new flux rotor based on model reference adaptive system for sensorless direct torque control modified for induction motor

Introduction. In order to realize an efficient speed control of induction motor, speed sensors, such as encoder, resolver or tachometer may be utilized. However, some problems appear such as, need of shaft extension, which decreases the mechanical robustness of the drive, reduce the reliability, and increase in cost. Purpose. In order to eliminate of speed sensors without losing. Several solutions to solve this problem have been suggested. Based on the motor fundamental excitation model, high frequency signal injection methods. The necessity of external hardware for signal injection and the adverse influence of injecting signal on the motor performance do not constitute an advantage for this technique. Fundamental model-based strategies method using instantaneous values of stator voltages and currents to estimate the rotor speed has been investigate. Several other methods have been proposed, such as model reference adaptive system, sliding mode observers, Luenberger observer and Kalman filter. The novelty of the proposed work consists in presenting a model reference adaptive system based speed estimator for sensorless direct torque control modified for induction motor drive. The model reference adaptive system is formed with flux rotor and the estimated stator current vector. Methods. The reference model utilizes measured current vector. On the other hand, the adjustable model uses the estimated stator current vector. The current is estimated through the solution of machine state equations. Practical value. The merits of the proposed estimator are demonstrated experimentally through a test-rig realized via the dSPACE DS1104 card in various operating conditions. The experimental results show the efficiency of the proposed speed estimation technique. Experimental results show the effectiveness of the proposed speed estimation method at nominal speed regions and speed reversal, and good results with respect to measurement speed estimation errors obtained.