پایداری N-سیگما در سیستم های تصادفی با کنترل مد لغزشی
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پایداری N-سیگما در سیستم های تصادفی با کنترل مد لغزشی

عنوان فارسی مقاله: پایداری N-سیگما در سیستم های تصادفی با کنترل مد لغزشی
عنوان انگلیسی مقاله: N-sigma stability of stochastic systems with sliding mode control
مجله/کنفرانس: Journal of the Franklin Institute
رشته های تحصیلی مرتبط:  مهندسی برق
گرایش های تحصیلی مرتبط:  مهندسی کنترل
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.jfranklin.2013.03.013
دانشگاه: Interdisciplinary Programme in Systems and Control Engg., Indian Institute of Technology Bombay, India
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2014
ایمپکت فاکتور: 1.916 در سال 2017
شاخص H_index: 64 در سال 2019
شاخص SJR: 1.322 در سال 2017
شناسه ISSN: 0016-0032
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 11
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11931
فهرست انگلیسی مطالب

Abstract


1. Introduction


2. Review of available literature


3. N-sigma stability


4. Simulation example


5. Conclusions


Acknowledgments


References

نمونه متن انگلیسی مقاله

Abstract


In this paper, sliding mode control for discrete time systems with stochastic noise in their input channel has been discussed. The idea of process control using control charts has influenced this new approach towards dealing with systems with stochastic noise. The new approach approximates the stochastic noise as a bounded uncertainty, similar to having bounds in the control charts for stochastic process control data. For discrete time systems, this results in a bounded stability in probability of the quasi sliding mode, which is referred to as the N-sigma bounded stability. The probability associated with the stability notions is not fixed and the control engineer may desire lower or higher degrees of stability in terms of this probability. Thus one has design flexibility while implementing the theory in practice, where one might have to adjust the desired degree of stability due to hardware limitations.


Introduction


Stochastic systems have been finding quite a lot of interest in the control community over the years. Researchers have attempted to develop the theory and control for stabilization of such systems in both continuous time and discrete time [1,2,4–8]. Several approaches have been taken by researchers, which can be broadly separated into their dealing of the system dynamics using ordinary difference [5–8] in case of discrete time systems and stochastic differential [1,2,4] in case of continuous time systems. All of them have been able to achieve either a notion of stability with certain probability [7,8] or have been able to assign the mean and covariance to them [6]. Some have proposed mean square stability or stochastic stability of the system [1,2,5]. Such and other stability ideas had been discussed in [13] in details.

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