Tuned Fuzzy Logic Control of Switched Reluctance Motor Drives
The switched reluctance motor (SRM) has gained much attention in the past few years over other types of electric motors in the drive applications due to its simple structure, ruggedness and inexpensive manufacturing potential. However, these merits are ov
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Tuned Fuzzy Logic Control of Switched Reluctance Motor Drives Nessy Thankachan and S. V. Reeba
Abstract The switched reluctance motor (SRM) has gained much attention in the past few years over other types of electric motors in the drive applications due to its simple structure, ruggedness and inexpensive manufacturing potential. However, these merits are overshadowed by its inherent high torque ripple, acoustic noise and difficulty to control. When the exact analytical model of the controlled system is uncertain or difficult to be characterized, intelligent control arts such as fuzzy logic control (FLC) may allow better performance compared to conventional controllers. In this paper a PI-like fuzzy logic speed controller with output scaling factor tuned, by an updating factor, based on fuzzy logic reasoning, is applied to an SRM drive system. A reduced rule base is used to simplify the program complexity of the controller without losing the system performance and stability. The nonlinear modeling of SRM is done based on look up tables with data obtained by finite element analysis. Keywords Fuzzy logic controller
Scaling factor Switched reluctance motor
79.1 Introduction Switched reluctance motor (SRM) can be a potential alternative to other conventional ac machines commonly used in various industries due to its unique characteristics in the aspects of mechanical simplicity in construction, high N. Thankachan (&) S. V. Reeba Department of EEE, College of Engineering, Trivandrum, India e-mail: [email protected] S. V. Reeba e-mail: [email protected]
V. V. Das (ed.), Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing, Lecture Notes in Electrical Engineering 150, DOI: 10.1007/978-1-4614-3363-7_79, Ó Springer Science+Business Media New York 2013
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efficiency, fault tolerance ability, high reliability and robustness in operation. However, due to the doubly salient structure and magnetic saturation, SRM acquaints with torque ripples, vibration and noise, which limits its application. It is however, difficult to control, due to its non-linear nature [1, 2]. Therefore, precise control of SRM is not easy using conventional methods like PI or PID controls as its flux linkage, inductance, and torque possess mutual coupling with rotor position and phase current. Hence, analytical or computerbased experimental determinations are often required to characterize the magnetization curves of the SRM. When the analytical model of the controlled system is vague or difficult to model, intelligent control techniques such as Fuzzy Logic Controller (FLC) gives better control performance [3]. The success of fuzzy logic controllers mainly lies in their ability to cope with knowledge represented in a linguistic form instead of representation in the conventional mathematical framework. The advantages of fuzzy logic includes robustness, a much wider range of operating conditions, operation with noise and disturbances of dif
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