کنترل همزمان ساز تطبیقی سیستم‌های غیرخطی آشوب در حضور اشباع ورودی و عیب عملگر
محورهای موضوعی : انرژی های تجدیدپذیر
مهناز هاشمی
1
(استادیار – دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
امیر پویان
2
(کارشناس ارشد - دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
کلید واژه: روش کنترلی گام به عقب, اشباع ورودی, سیستم‌های آشوب, عیب عملگر, کنترل تطبیق,
چکیده مقاله :
در این مقاله، یک کنترل کننده تطبیقی برای کنترل سیستم‌های غیرخطی جرک در معرض پارامترهای نامعین و محدودیت‌های کنترلی عیب عملگر و اشباع ورودی ارائه شده است. عیب عملگر در نظر گرفته شده عیوب کاهش کارایی و قفل شونده را پوشش می‌دهد. مقدار، زمان و الگوی عیوب در نظر گرفته شده کاملاً نامعین است یعنی مشخص نیست در چه زمانی، کدام عملگرها و با چه وضعیتی دچار عیب می‌شوند.کنترل کننده تطبیقی مقاوم پیشنهادی بر اساس روش کنترلی گام به عقب طراحی شده است. در این مقاله، با معرفی توابع لیاپانوف- کراسوسکی جدید، کرانداری سیگنالهای سیستم حلقه بسته و همگرایی خطای تعقیب به یک همسایگی نزدیک مبدأ تضمین شده است. روش تطبیقی پیشنهادی، عیوب عملگر را بدون نیاز به واحد تشخیص عیب جبران می‌کند. نتایج شبیه سازی، کارایی و صحت روش کنترلی ارائه شده را در همزمان سازی سیستم آشوب در حضور عیب عملگر، اشباع ورودی و نامعینی پارامتری نشان می‌دهد.
In this paper, the control problem is investigated for Jerk chaotic systems against unknown parameters, actuator faults and input saturation. The considered actuator fault covers both of the stuck faults and loss of effectiveness faults in actuators. The values, times and patterns of the considered faults are completely unknown. That is, during the system operation it is unknown when, by how much and which actuators fail. A robust adaptive controller is presented based on the backstepping design method to achieve complete synchronization of the identical Jerk chaotic systems. By introducing the new Lyapunov functions, it is proved that all the closed loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The proposed adaptive method compensates the actuator faults without any need for explicit fault detection. Simulation results represent that the designed controller can synchronize the identical chaotic systems in the presence of actuator fault, input saturation and unknown parameters.
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_||_[1] B. E. Nieuwenhuys, A. C. Gluhoi, “Chaos, oscillations and the golden future of catalysis”, Catalysis Today,Vol. 100,pp. 49-54,Feb. 2005(doi.org/10.1016/j.cattod.2004.12.022).
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[3] H. Zhang, X. Ma, B. Xue, W. Liu, “Study of intermittent bifurcations and chaos in boost PFC converters by nonlinear discrete models”, ChaosSolutions Fractals, Vol. 23, No. 2, pp. 433-444, Jan. 2005 (doi:/10.1016/j.chaos.2004.04.020).
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[9] S. Vaidyanathan,A. TaherAzar, “Adaptive Backstepping control and Synchronization of a Novel 3-D Jerk System with an exponential Nonlinearity”, Advances in Chaos Theory and Intelligent Control, Stud. Fuzz.Soft Comput. V. 337, pp. 249-274, Apr.2016(doi.org/10.1007/978-3-319-30340-6_11).
[10] H. Li,X. Liao, C. Li, “Chaos control and synchronization via a novel chatter free sliding mode strategy”, Neurocomputing Vol. 74, No. 17,pp. 3212–3222, Oct. 2011(doi.org/10.1016/j.neucom.2011.05.002).
[11] M. Pourmahmood, S.Khanmohammadi, G. Alizadeh, “Synchronization of two different uncertain chaotic systems with unknown parameters using a robust adaptive slidingmode controller”, Commun. Nonlinear Sci. Numer. Simulation Vol. 16, No. 7, pp. 2853–2868, Jul. 2011(doi.org/10.1016/j.cnsns.2010.09.038).
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[21] P. Li, G. Yang,"Backstepping adaptive fuzzy control of uncertain nonlinear systems against actuator faults", J. Control Theory Appl,Vol. 7, No. 3, pp. 248–256, Aug. 2009(doi.org/10.1007/s11768-009-8074-6).
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[29] F. Z. Chaoui,F. Giri,M. M’Saad, J. M. Dion, “Adaptive tracking with saturating input and controller integration action”, IEEE Trans. Automatic Control, Vol. 43,No. 11, pp. 1638–1643,Nov. 1998(doi.10.1109/9.728887).
[30] Y. Zhang, C. Wen, Y. Soh, “Adaptive backstepping control design for systems with unknown high-frequency gain”, IEEE Trans. Automatic Control.Vol. 45, No. 12, pp. 2350–2354, Dec. 2000(doi.10.1109/9.895572).
[31] T. Li,R. Li,J. Li, “Decentralized adaptive neural control of nonlinear interconnected large scale systems with unknown time delays and input saturation”, Neurocomputing,Vol. 74, No. 14-15, pp. 2277–2283,Jul. 2011(doi.org/10.1016/j.neucom.2011.03.005).
[32] Y. Li, S. Tong, T. Li, “Direct adaptive fuzzy backstepping control of uncertain nonlinear systems in the presence of input saturation”, Neural Comput. Appl, Vol. 23, No. 5, pp. 1207–1216, Jun. 2012 (doi.org/10.1007/s00521-012-0993-3).
[33] H. Wang,B. Chen, X. Liu, K. Liu, C. Lin, “Adaptive neural tracking control for stochastic nonlinear strict-feedback systems with unknown input saturation”, Inf. Sci,Vol. 269, pp. 300–315,Jun. 2014 (doi.org/10.1016/j.ins.2013.09.043).
[34] S. Li, Z. Xiang, “Adaptive Prescribed performance control for switched nonlinear systems with input saturation”, Int. J. Syst. Science, Vol. 49, No. 1,pp. 111-123, Oct. 2018 (doi.org/10.1080/00207721.2017.1390706).
[35] A. Peydayesh, M. Arefi, H. Modares, “Distributed neuro-adaptive control protocols for non-strict feedback nonlinear MASs with input saturation”, IET Control Theory Appl,Vol. 12, No. 11,pp. 1611-1620,Jul. 2018(doi.10.1049/iet-cta.2017.0875).