IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) ISSN: 2278-1676 Volume 3, Issue 6 (Nov. - Dec. 2012), PP 41-48 www.iosrjournals.org
Speed Control of Dc Motor Using Fuzzy Logic Technique
1, 2, 3
J. N. Rai, 2Mayank Singhal, 3MayankNandwani
Department of Electrical Engineering, Delhi Technological University
Abstract:This project uses FUZZY LOGIC TECHNIQUE in estimating speed and controlling it for DC motor. The rotor speed of the dc motor can be made to follow an arbitrarily selected trajectory. The purpose is to achieve accurate trajectory control of the speed of DC Motor, especially when the motor and load parameters are unknown. Such a control scheme gives very accurate and precise result in very short time. The fuzzy logic controller employs if-else form programming of the various conditions to control the motor speed.
Direct current (DC) motors have been widely used in many industrial applications such as electric vehicles, steel rolling mills, electric cranes, and robotic manipulators due to precise, wide, simple and continuous control characteristics. The development of high performance motor drives is very important in industrial as well as other purpose applications. Generally, a high performance motor drive system must have good dynamic speed command tracking and load regulating response. DC drives, because of their simplicity, ease of application, reliability and favourablecost have long been a backbone of industrial applications. DC drives are less complex with a single power conversion from AC to DC. DC drives are normally less expensive for most horsepower ratings. DC motors have a long tradition of use as adjustable speed machines and a wide range of options have evolved for this purpose. In these applications, the motor should be precisely controlled to give the desired performance. Traditionally rheostatic armature control method was widely used for the speed control of low power dc motors. However the controllability, cheapness, higher efficiency, and higher current carrying capabilities of static power converters brought a major change in the performance of electrical drives. Many varieties of control schemes such as P, proportional integral (PI), proportional derivation integral (PID), adaptive, and fuzzy logic controller (FLCs), have been developed for speed control of dc motors. As PID controllers require exact mathematical modelling, the performance of the system is questionable if there is parameter variation. In recent years neural network controllers (NNC) were effectively introduced to improve the performance of nonlinear systems. The proposed controller systems consist of multi-input fuzzy logic controller (FLC) and multi-input integrated fuzzy logic controller (IFLC) for the speed control. Motor Control Constraints 1. Non linearity in dc motor 2. Variable and unpredictable inputs 3. Noise propagation along a series of unit processes 4. Unknown parameters 5. Changes in load dynamics Major problems in applying a conventional control algorithm in a speed controller are the effects of non-linearity in a DC motor. The non-linear characteristics of a DC motor such as saturation and friction could degrade the performance of conventional controllers. Many advance model-based control methods such as variable-structure control and model reference adoptive control have been developed to reduce these effects. However, the performance of these methods depends on the accuracy of system models and parameters. Generally, an accurate non-linear model of an actual DC motor is difficult to find, and parameter values obtained from system identification may be only approximate values. Even the PID controllers require exact mathematical modelling. Advantage of using fuzzy technique 1. Inherent approximation capability 2. High degree of tolerance 3. Smooth operation 4. Reduce the effect of Non-linearity Fast adaptation 5. Learning ability www.iosrjournals.org...
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FUZZY AND SOFT COMPUTING- Jyh-shing, Roger Jang, Chuen-Tsai Sun K. B. Mohanty,“Fuzzy remote controller for converter DC motor drives” , Parintantra, Vol. 9, No. 1, June 2004 Zimmermann, H. (2001). Fuzzy Set Theory And Its Applications. Boston: Kluwer Academic Publishers. ISBN 0-7923-7435-5 Yager, Ronald R, Filev, Dimitar P. (1994). Essentials of Fuzzy Modelling and Control. New York: Wiley. ISBN 0 -471-01761-2 Santos, Eugene S. (1970). “Fuzzy Algorithms”. Information and Control 17 (4) B.C Kuo–Digital Control Systems-2 nd Edition
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