Top PDF Fuzzy logic based soft starting of induction motor with current control

Fuzzy logic based soft starting of induction motor with current control

Fuzzy logic based soft starting of induction motor with current control

Induction Motors are the most commonly used machines in industries mainly because it is robust, inexpensive and easy to maintain. For an Induction Motor, the starting current is around ten times the rated current and this persists for a few cycles. This may be very much detrimental for the machine and hence there is a need for using starters to limit the starting current. During earlier times, mechanical starters like star delta, direct online and autotransformer starters were used. Thyristorized soft starters are of low cost. Their reliability is on the higher side and they are simple and occupy lesser space, and hence their use is a fruitful solution to the induction motor starting problem. The ac motor starters incorporating power semiconductors are used frequently nowadays for their controlled soft starting ability with reduced starting current. In this study, a closed loop MATLAB SIMULINK model is developed which would reduce the current at the soft starting period. A fuzzy logic based soft start scheme for induction motor drives is used which would give optimal performance. Fuzzy logic has received higher emphasis in the field of power electronics because of its adaptive capability. The three phase stator currents are converted into two phase currents. The magnitude of current is then converted to per unit. Then it is compared with a reference value. The error is passed through a Fuzzy Logic Controller (FLC).The FLC output is used to control the amplitude of the reference sine wave. Hence by controlling the modulation index, the applied voltage to the stator is controlled and hence the starting current is limited.
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Fuzzy Logic based Soft Starting of Three Phase Induction Motor

Fuzzy Logic based Soft Starting of Three Phase Induction Motor

ABSTRACT: This paper presents the current control of three phase induction motor using Fuzzy logic. A thyristorized AC voltage Inverter is utilized as the starting equipment while the motor current regulation is carried out using an optimally tuned Proportional-Integral (PI) controller. AC voltage Inverter fed starting of induction motor is a non-linear process. The complete drive system including AC voltage inverter fed induction motor in conjunction with PI controller is optimized using Fuzzy logic. The successful implementation with a low cost microcontroller demonstrates the feasibility of the new approach. The simulation is carried out by Matlab/Simulink.
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Fuzzy Logic Applications on Induction Motor for Axis Control

Fuzzy Logic Applications on Induction Motor for Axis Control

The IM starts in closed-loop because speed and current control are in cascade. The load torque applied to the machine's shaft is set to its nominal value. Two control loops are used: the inner loop regulates the motor's stator currents and the outer loop controls the motor's speed. Using a PWM inverter, a noise is observed in the electromagnetic torque waveform. However, the motor's inertia prevents this noise from appearing in the motor's speed waveform (Fig. 7). The stator currents are quite "noisy," which is to be expected when using PWM inverters. The rotor speed increases fast to its synchronous value after few oscillations and preserves its value (Fig. 7). The current takes initially a high value in order to develop the kinetic energy to accelerate the rotor. After a certain delay the current stabilizes to their nominal value. The reference speed of the motor is set at 700 rad/sec. As soon as the speed reaches the reference speed the PI speed controller forces the speed to remain steady at reference speed. During starting period torque climbs to maximum capability of the motor after that it settles down to steady state value of 3 N-M. Also the current rises to its maximum value and after that comes back to nearly 2 A.
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Sensorless Control of Induction Motor Drives at Low Speed Using Fuzzy Logic Control

Sensorless Control of Induction Motor Drives at Low Speed Using Fuzzy Logic Control

Nowadays, AC motors, in particular squirrel cage induction type, are widely used in industry due to their simple and rugged structure. Moreover, they are economical and immune to heavy overloads. However the use of induction motors also has its disadvantages, mainly the controllability, due to its complex mathematical model and its nonlinear behavior during saturation effect. Induction motor (IM) require complex control algorithms, because there is no linear relationship between the stator current and either the torque or the flux. This means that it is difficult to control the speed or the torque. So the development of high performance motor drives to control such motor is very important in industrial applications, high performance control and estimation techniques for induction motor drives are very fascinating and challenging subjects and recently many techniques have been developed for induction motor drives and hence very good control performances have been achieved. Generally, a high performance drive system must have good dynamic speed command tracking and load regulating responses, and the performances are insensitive to the drive and load parameter variations. Among the existing techniques, the most commonly used is the proportional-integral (PI) controller. The PI controller is very easy to be implemented, but the PI controller cannot lead to good tracking and regulating performance simultaneously. Moreover, its control performances are sensitive to the system parameter variation and load disturbances. Recently the modern controls, such as optimal control, variable structure System control, adaptive control, etc., have been applied to yield better performance. However, the desired drive specifications still cannot be perfectly satisfied by these methods.
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Speed Control of Three Phase Induction Motor Using Fuzzy Logic Techniques

Speed Control of Three Phase Induction Motor Using Fuzzy Logic Techniques

Abstract— Induction motor is widely used in industries because of its high robustness, reliability, lower cost, high efficiency, self starting capability and simplicity. Most of the applications require fast and smart system for control of speed. The speed control of induction motor is important to achieve maximum torque and high efficiency. In many motor control applications use of fuzzy logic (Soft computing technique) is gaining interest due to its non- linearity handling features and independence of the plant modeling. The fuzzy logic controller relies on a set of simple linguistic if-then rules based on expert’s knowledge. This paper presents a rule- based Mamdani type FLC applied to scalar closed loop volt/Hz induction motor control with slip regulation and its simulation results. Various toolboxes in Matlab are used for testing the simulated design.
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Speed Control and Parameter Variation of Induction Motor Drives using Fuzzy Logic & ADRC Controllers

Speed Control and Parameter Variation of Induction Motor Drives using Fuzzy Logic & ADRC Controllers

To tackle the load balancing problem, conventional control theory can be applied to restore system equilibrium. Fuzzy logic control attempts to capture intuition in the form of IF-THEN rules, and conclusions are drawn from these rules. Based on both intuitive and expert knowledge, system parameters can be modelled as linguistic variables and their corresponding membership functions can be designed. Thus, nonlinear system with great complexity and uncertainty can be effectively controlled based on fuzzy rules without dealing with complex, uncertain, and error-prone mathematical models. In this paper mamdani model is used for the speed control of induction motor.
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Speed Control For Direct Current (DC) Motor With An Approach Of Fuzzy Logic

Speed Control For Direct Current (DC) Motor With An Approach Of Fuzzy Logic

The PI and PID controller have been used as control method of servo driving in industrial control field. The driving specific property can be indicated well, a control constant is a property set. But the property constant need to be changed in order to maintain the optimum driving state if the driving point or the motor parameters are changed. Recently the fuzzy controller has appeared which is based on knowledge or an experience of expert rather than on a complicate mathematical modeling. The fuzzy controller works well using experimental information even if not having mathematical modeling. Moreover, the fuzzy controller is capable of real time control using fuzzy rule base.
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Induction motor modelling using fuzzy logic

Induction motor modelling using fuzzy logic

In order to understand and analyze vector control, the dynamic model of the induction motor is necessary. It has been found that the dynamic model equations developed on a rotating reference frame is easier to describe the characteristics of induction motors. Any method for speed prediction is based on a model of the motor and the drive. The best accuracy of prediction for an induction motor is needed. Today, there are many choices of modelling techniques. One of them is system identification where it identifies the behaviour of a given system by estimating the model from input and output data. The estimated model is useful to simulate and predict the behaviour of the system. Not limited to that, the fitted model can be employed to regulate the output of plant.
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Performance Analysis of Speed Control of Induction Motor using Pi, SMC & Fuzzy Logic Controller

Performance Analysis of Speed Control of Induction Motor using Pi, SMC & Fuzzy Logic Controller

performance, variable speed applications due to its low cost, low maintenance, robustness and reliability. IMs perform satisfactory with the vector control strategy for wide range of speed applications and fast torque response. Because of the higher order unmodeled system dynamics and different machine parameters such as rotor speed, stator and rotor resistance variation and load torque variation, different nonlinear controllers are used to increase its robustness and to make the system stable. However, the use of linear controllers such as PI controller does not give satisfactory performance due to the above causes. Sliding Mode Controller and Fuzzy Logic Controller are designed for robust control of IMs. Both controllers performance is satisfactory under different adverse condition. But, the main disadvantage of sliding mode controller is the chattering problem that can be reduced by taking necessary steps. This work is based on investigation and evaluation of the performance of a IMs drive controlled by PI, Sliding mode and Fuzzy logic speed controllers
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Induction motor controller using fuzzy logic

Induction motor controller using fuzzy logic

Fuzzy logic approach allows the designer to handle efficiently very complex closed-loop control problems, reducing in many cases, engineering time and cost. It has the ability to distribute gain over a range of inputs in order to avoid the saturation of the control capability. Fuzzy logic shoved very useful to solved nonlinear control problems. It’s also allows a simpler and more robust control solution whose performance can only be matched by a classical controller with adaptive characteristics. The advantages provided by a FLC is it operates in a knowledge – based way and its knowledge relies on a set of linguistic such as if-then rules like a human logic.
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Modelling and Control Strategy of Induction Motor Using Fuzzy Logic Control Technique

Modelling and Control Strategy of Induction Motor Using Fuzzy Logic Control Technique

A systematic approach of achieving robust speed control of an induction motor drive by means of Takagi-Sugeno based fuzzy control strategy has been investigated in this paper. Simulink models were developed in Matlab 7 with the TS-based fuzzy controllers (hybrid controller) for the speed control of IM. The control strategy was also developed by writing a set of 49 fuzzy rules according to the TS control strategy. The main advantage of designing the TS based fuzzy coordination scheme to control the speed of the IM is to increase the dynamic performance & provide good stabilization. Simulations were run in Matlab 7 & the results were observed on the corresponding scopes. Graphs of speed, torque, stator current, flux, etc. vs. time were observed. The outputs take less time to stabilize, which can be observed from the simulation results. But, from the incorporation of the TS based fuzzy coordination system in loop with the plant gave better results there by stabilizing the plant very quickly. The developed control strategy is not only simple, reliable, and may be easy to implement in real time applications, but also cost-effective as when this control scheme is implemented in real time, the size of the controller will become very small. Collectively, these results show that the TSfuzzy controller provides faster settling times, has very good dynamic response & good stabilization.
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A fuzzy logic controller to line starting performance synchronous motor for a crane system using vector control

A fuzzy logic controller to line starting performance synchronous motor for a crane system using vector control

Many fuzzy logic controller used in automation control cause employing knowledge base and linguistic expression that be able to represented human operator work mechanism. Controlling with fuzzy logic derived heuristically based on process condition and operator experiences then did not need mathematic model from plant that will be controlled [7]. Fuzzy logic controller expected is able to changes value automatically from PID controller parameter appropriate with changes happen on system, then system performance, in this case settling time, rise time, and error steady state suitable with wished criteria. From description above emerge a problem in implementing fuzzy logic to change gain PID controller value in three phases induction motor speed control to get expected performance.
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Fuzzy Logic Based Speed Control of Induction Motor Considering Core Loss into Account

Fuzzy Logic Based Speed Control of Induction Motor Considering Core Loss into Account

Induction motors have been considered for many indus- trial applications since its discovery [1-3] because of its low maintenance, robustness, low cost, high efficiency, good self starting, simplicity of design, absence of the collector brooms system and small inertia. The most pro- blematic things of induction motor are its complex, non- linear, multivariable mathematical model, and it is not inherently capable of providing variable speed operation [4,5]. The problems of induction motor can be solved through the use of intelligent control and adjustable speed controllers, such as scalar and vector control drive [6,7].
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Speed Control of Single Phase Induction Motor Using Fuzzy Logic Controller

Speed Control of Single Phase Induction Motor Using Fuzzy Logic Controller

The Proportional Integral (PI) controller of A.C drives are commonly employed in industries and many other applications, because of its simplicity, but it does not give high degree of speed control of single phase induction motor. The intelligent control systems become a powerful tool for control nonlinear system in present time. This paper proposes the Proportional Integral (PI) controller designed based on fuzzy logic system as an intelligent speed controller of single phase induction motor. In addition, The mathematical modeling and simulation single phase induction motor capacitor run type as asymmetrical two phase induction motor is represented too Also, two phase four leg voltage source PWM inverter with Sinusoidal Pulse Width Modulation (SPWM) switching technique is demonstrated and simulated. The overall system for the proportional-integral (PI) controller designed based on fuzzy logic system is simulated using MATLAB/Simulink environment. The results shows high response of the controller to the change of load which make a wide range of speed variation and make the speed return to its reference value.
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THE ART OF INTELLIGENT CONTROL OF INDUCTION MOTOR DRIVE USING FUZZY LOGIC CONTROLLER

THE ART OF INTELLIGENT CONTROL OF INDUCTION MOTOR DRIVE USING FUZZY LOGIC CONTROLLER

Simulink because S-function programming knowledge is required to access the model variables. Another approach is using the Simulink Power System Block set [3] that can be purchased with Simulink. This block set also makes use of S- functions and is not as easy to work with as rest of the Simulink blocks. Reference [4] 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. Though induction motors have few advantageous characteristics, they also posse's nonlinear and time- varying dynamic interactions [5 6], Using conventional PI controller, it is very difficult and complex to design a high performance induction motor drive system. The fuzzy logic control (FLC) is attractive approach, which can accommodate motor parametric variations and difficulty in obtaining an accurate mathematical model of induction motor due to rotor parametric and load time constant variations. The FLC is a knowledge-based control that uses fuzzy set theory and fuzzy logic for knowledge representation [7]. This paper presents a fuzzy logic controller suitable for speed control of induction motor drives.
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Performance Enhancement of Fuzzy Logic Duty Ratio Controller Based Direct Torque Control of Induction Motor

Performance Enhancement of Fuzzy Logic Duty Ratio Controller Based Direct Torque Control of Induction Motor

network controllers [13-16]. It results in improved transient response but there is nothing much reduction in torque and flux ripples under steady state. Improvements in steady state performance of CDTC using ANN based switching controller is proposed in [17-18]. Improvements in CDTC using fuzzy logic switching controller is proposed by [3, 19- 22] and using hybrid AI technique is given by [23-24]. Implementation of ANN requires training of neural network for set of inputs and outputs based on black box approach. The performance of ANN depends upon selected structure of ANN number of iterations. The drawback on ANN based switching controller is, it does not give heuristic knowledge of process in selection of optimal switching vector. We can overcome the drawback of ANN using hybrid neuro-fuzzy switching controller. In neuro-fuzzy the architecture of system is known and system behavior is decided by fuzzy rule base generated based on ANN. Neuro fuzzy useful for systems whose domain knowledge is not known or expert cannot formulate the rule base. The selection of optimal switching state in DTC is well defined and shifting of one switching state to another depends upon torque error, flux error and stator flux space sector. Fuzzy logic is knowledge based system gives heuristic reasoning of the process. Fuzzy logic is an excellent tool to handle uncertainty in nonlinear function. Fuzzy logic process is easy to understand due to its simple logical structure and inference mechanism.
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Comparison of Fuzzy Logic & Hybrid Controller based DTC Technique of Induction Motor

Comparison of Fuzzy Logic & Hybrid Controller based DTC Technique of Induction Motor

The first phase of the two is training phase; it is used to generate training data set. In the mentioned control approach, the actual and change in torque of motor values are generated in form of vector and data is provided to neural network. Then the data is trained by back propagation training algorithm with respect to the actual torque of motor. Then the trained data is applied to the fuzzy interference system for generating the control fuzzy rules. In ANFIS, the fuzzy rules base control interference system is automatically generated.
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Torque Control Strategy for Induction Motor Based On Fuzzy System

Torque Control Strategy for Induction Motor Based On Fuzzy System

Abstract: The paper provides a torque control strategy for induction motor drives. The strategy makes use of fuzzy based switching pattern for the converter switches for conversion of the DC to AC. The converter is a conventional six switch converter topology. Out of the six switches two will be capacitors and other switches will be IGBTs. The reduction in the number of switch will reduce the losses. Steady state operation of the induction motor is maintained by PI and fuzzy logic controllers.
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Fuzzy Logic Controller Based Direct Torque Control of PMBLDC Motor

Fuzzy Logic Controller Based Direct Torque Control of PMBLDC Motor

Abstract: Direct Torque Control of the sensor less permanent magnet brushless DC motor with fuzzy logic speed controller is presented in this paper. The direct torque control is one of the vector based control in variable frequency drive control. This project proposes the control of torque directly and stator flux amplitude indirectly by using direct axis current. The torque error, flux error and estimated position of the stator flux linkage vector are used to change the switching sequence of the inverter through space vector pulse width modulation to control the motor speed. An impedance source network is coupled between the inverter and the DC supply to reduce the ripples in torque by boosting the inverter’s input voltage. The three phase voltage and current are converted to d- q axis to calculate the stator flux linkage vector, rotor angle and actual torque using park transformation. A fuzzy logic controller is used to generate a reference torque value by processing the speed errors. A prototype model is made for 24 V BLDC motor and the closed loop operation is obtained using fuzzy logic controller.
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Performance Comparison of PI and Fuzzy-PI Logic Speed Control of Induction Motor

Performance Comparison of PI and Fuzzy-PI Logic Speed Control of Induction Motor

A proportional–integral–derivative controller (PI controller) is a generic control loop feedback mechanism widely used in industrial control systems – a PI is the most commonly used feedback controller. A PI controller calculates an "error" value as the difference between a measured process variable and a desired set point. The controller attempts to minimize the error by adjusting the process control inputs. In the absence of knowledge of the underlying process, PI controllers are the best controllers. However, for best performance, the PI parameters used in the calculation must be tuned according to the nature of the system – while the design is generic, the parameters depend on the specific system. The PI controller calculation involves three separate parameters, and is accordingly sometimes called three-term control: the proportional, the integral and derivative values, denoted P, I, and D. The proportional value determines the reaction to the current error, the integral value determines the reaction based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element. Heuristically, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction of future errors, based on current rate of change. By tuning the three constants in the PI controller algorithm, the controller can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability. The PID controller is probably the most-used feedback control design. PID is an acronym for Proportional-Integral-Derivative, referring to the three terms operating on the error signal to produce a control signal. If u(t) is the control signal sent to the system, y(t) is the measured output and r(t) is the desired output, and tracking error e(t) = r(t) − y(t), a PID controller has the general form.
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