Induction Motors are the most commonly used machines in industries mainly because it is robust, inexpensive and easy to maintain. For an InductionMotor, the startingcurrent 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 startingcurrent. 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 inductionmotorstarting problem. The ac motor starters incorporating power semiconductors are used frequently nowadays for their controlled softstarting ability with reduced startingcurrent. In this study, a closed loop MATLAB SIMULINK model is developed which would reduce the current at the softstarting period. A fuzzylogicbasedsoft start scheme for inductionmotor drives is used which would give optimal performance. Fuzzylogic 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 FuzzyLogic 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 startingcurrent is limited.
ABSTRACT: This paper presents the currentcontrol of three phase inductionmotor using Fuzzylogic. A thyristorized AC voltage Inverter is utilized as the starting equipment while the motorcurrent regulation is carried out using an optimally tuned Proportional-Integral (PI) controller. AC voltage Inverter fed starting of inductionmotor is a non-linear process. The complete drive system including AC voltage inverter fed inductionmotor in conjunction with PI controller is optimized using Fuzzylogic. The successful implementation with a low cost microcontroller demonstrates the feasibility of the new approach. The simulation is carried out by Matlab/Simulink.
The IM starts in closed-loop because speed and currentcontrol 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.
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. Inductionmotor (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 inductionmotor drives are very fascinating and challenging subjects and recently many techniques have been developed for inductionmotor 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.
Abstract— Inductionmotor 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 inductionmotor is important to achieve maximum torque and high efficiency. In many motorcontrol applications use of fuzzylogic (Soft computing technique) is gaining interest due to its non- linearity handling features and independence of the plant modeling. The fuzzylogic 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 inductionmotorcontrol with slip regulation and its simulation results. Various toolboxes in Matlab are used for testing the simulated design.
To tackle the load balancing problem, conventional control theory can be applied to restore system equilibrium. Fuzzylogiccontrol 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 inductionmotor.
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.
In order to understand and analyze vector control, the dynamic model of the inductionmotor 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 inductionmotor 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.
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 FuzzyLogic 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 Fuzzylogic speed controllers
Fuzzylogic 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. Fuzzylogic 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.
A systematic approach of achieving robust speed control of an inductionmotor drive by means of Takagi-Sugeno basedfuzzycontrol strategy has been investigated in this paper. Simulink models were developed in Matlab 7 with the TS-basedfuzzy 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 basedfuzzy 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 basedfuzzy 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.
Many fuzzylogic controller used in automation control cause employing knowledge base and linguistic expression that be able to represented human operator work mechanism. Controlling with fuzzylogic derived heuristically based on process condition and operator experiences then did not need mathematic model from plant that will be controlled . Fuzzylogic 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 fuzzylogic to change gain PID controller value in three phases inductionmotor speed control to get expected performance.
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 inductionmotor are its complex, non- linear, multivariable mathematical model, and it is not inherently capable of providing variable speed operation [4,5]. The problems of inductionmotor can be solved through the use of intelligent control and adjustable speed controllers, such as scalar and vector control drive [6,7].
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 inductionmotor. 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 fuzzylogic system as an intelligent speed controller of single phase inductionmotor. In addition, The mathematical modeling and simulation single phase inductionmotor capacitor run type as asymmetrical two phase inductionmotor 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 fuzzylogic 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.
Simulink because S-function programming knowledge is required to access the model variables. Another approach is using the Simulink Power System Block set  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  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 inductionmotor 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 inductionmotor drive system. The fuzzylogiccontrol (FLC) is attractive approach, which can accommodate motor parametric variations and difficulty in obtaining an accurate mathematical model of inductionmotor due to rotor parametric and load time constant variations. The FLC is a knowledge-basedcontrol that uses fuzzy set theory and fuzzylogic for knowledge representation . This paper presents a fuzzylogic controller suitable for speed control of inductionmotor drives.
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 fuzzylogic 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. Fuzzylogic is knowledge based system gives heuristic reasoning of the process. Fuzzylogic is an excellent tool to handle uncertainty in nonlinear function. Fuzzylogic process is easy to understand due to its simple logical structure and inference mechanism.
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 controlfuzzy rules. In ANFIS, the fuzzy rules base control interference system is automatically generated.
Abstract: The paper provides a torque control strategy for inductionmotor drives. The strategy makes use of fuzzybased 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 inductionmotor is maintained by PI and fuzzylogic controllers.
Abstract: Direct Torque Control of the sensor less permanent magnet brushless DC motor with fuzzylogic speed controller is presented in this paper. The direct torque control is one of the vector basedcontrol 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 fuzzylogic 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 fuzzylogic controller.
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.