کنترل هوشمند هلی‌کوپتر چهار موتور بدون سرنشین در حالت معلق در هوا
محورهای موضوعی : کنترل هوشمند
1 - دانشگاه آزاد اسلامی واحد نجف آباد
2 - دانشگاه آزاد اسلامی واحد نجف آباد
کلید واژه: مدل‌سازی, کنترلکننده فازی, کنترلکننده عصبی- فازی, کنترلکننده PID, کوادروتور,
چکیده مقاله :
هلی‌کوپتر چهار موتور (کوادروتور)، یک هواپیمای بدون سرنشین با چهار موتور است. به دلیل قابلیت‌های منحصر به‌فرد این وسیله از جمله نقصان تحریک بودن، پرواز و فرود عمودی، حرکت درجا، درجات آزادی بیشتر و کاربردهای نظامی و غیرنظامی، توجه ویژه بسیاری از محققین را به‌خود معطوف کرده است. به‌دلیل دینامیک غیرخطی و پیچیده این سیستم چندمتغیره با شش درجه آزادی، مدل‌سازی و کنترل این وسیله یکی از زمینه‌های چالش‌برانگیز در مهندسی کنترل به شمار می‌آید. در این مقاله مدل‌سازی کوادروتور با استفاده از معادلات نیوتن- اویلر توصیف می‌گردد. پایدارسازی و کنترل ارتفاع و وضعیت این وسیله توسط سه کنترلکننده PID کلاسیک، فازی- PID و فازی- عصبی مبتنی بر PID صورت می‌پذیرد و همچنین عملکرد این کنترل‌کننده‌ها در حضور اغتشاش و نامعینی جرمی مورد بررسی قرار می‌گیرند. هدف اصلی این مقاله طراحی الگوریتم PID هوشمند می‌باشد که از تلفیق منطق فازی و شبکه‌های عصبی ساخته شده و کنترلکننده فازی- عصبی مبتنی بر PID را مطرح می‌نماید. نتایج شبیه‌سازی‌های صورت گرفته توسط نرم‌افزار MATLAB ارائه می‌شوند.
A Quadrotor helicopter is an unmanned aerial vehicle (UAV). This vehicle has attracted lots of researchers’ attention because of its unique abilities such as being an under-actuated system, vertical take-off and landing, spot movement, more degree of freedom (DOF) and military and non- military functions. Because of nonlinear and complex dynamic, modeling and controlling this vehicle is one of the most challenging areas in control engineering. In this paper modeling of a Quadrotor will be described using Newton-Euler equations. Stabilizing and controlling of altitude and its attitude are done by three controller including classic PID, Fuzzy- PID and Neural- Fuzzy based on PID. Performances of these controllers are analyzed in the presence of disturbances and mass uncertainties. The main aim of this paper is designing an intelligent PID algorithm which is made by combining fuzzy logic and neural system and it will introduce a Neural- Fuzzy controller which is based on PID. Simulation results are presented by MATLAB software.
[1] M.W. Bailey, “Unmanned aerial vehicle path planning and image processing for orthoimagery and digital surface model generation”, Master’s thesis, Vanderbilt University, Nashville, Dec. 2012.
[2] S. Raza, G. Wail, “Intelligent flight control of an autonomous quadrotor, motion control”, University of Ottawa: In Tech, federico casolo ed, 2010.
[3] S. Gonz´alez-V´azquez, J. Moreno-Valenzuela, “A new nonlinear pi/pid controller for quadrotor posture regulation”, Proceeding of the IEEE/CERMA, pp. 642 – 647, Morelos, Oct. 2010.
[4] J. Li, Y. Li, “Dynamic analysis and pid control for a quadrotor”, Proceeding of the IEEE/ICMA, pp.573 –578, Beijing, Aug. 2011.
[5] S. Bouabdallah, A. Noth, R. Siegwart, “PID versus LQ control techniques applied to an indoor micro quadrotor”, Procedding of the IEEE/IROS, Vol. 3, pp.2451 – 2456, Sendai, Japan, Oct 2004.
[6] S. Khatoon, D. Gupta, L. Das, “PID LQR control for a quadrotor: Modeling and simulation”, Proceeding of the IEEE/ICACCI, pp.769– 802, New Delhi, Oct. 2014.
[7] S. Vaitheeswaran, R. Mekala, “Non-linear attitude control methods for quadrotor MAVs: A study”, Proceeding of the IEEE/CCIP, pp.1–6, Noida, March 2015.
[8] H. Noshahri, H. Kharrati, “PID controller design for unmanned aerial vehicle using genetic algorithm,” Proceeding of the IEEE/ISIE, pp.213– 217, Istanbul, June 2014.
[9] B. Erginer, A. Erdin, “Design and implementation of a hybrid fuzzy controller for a quadrotor vtol vehicle”, International Journal of Control, Automation and Systems, Vol. 10, pp.61–70, Feb. 2012.
[10] E.A. Seidabad, S. Vandaki, A.V. Kamyad, “Designing fuzzy PID controller for quadrotor”, International Journal of Advanced Research in Computer Science Technology, Vol. 2, pp. 221– 227, Dec. 2014.
[11] M. Mirzaei, F.S. Nia, H. Mohammadi, “Applying adaptive fuzzy sliding mode control to an underactuated system”, Proceeding of the IEEE/ICIA, pp.654- 659, Shiraz, 2011.
[12] M. Efe, “Neural network assisted computationally simple PID control of a quad rotor uav”, IEEE Trans. on Industrial Informatics, Vol. 7, pp.354 –361, April. 2011.
[13] N. Raharja, Iswanto, M. Faris, A. Cahyadi, “Hover position quadrotor control with fuzzy logic”, Proceeding of the IEEE/ICITACEE, pp.89– 92, Semarang, Nov. 2014.
[14] A. Rabhi, M. Chadli, C. Pegard, “Robust fuzzy control for stabilization of a quadrotor”, Proceeding of the IEEE/ICAR, pp.471 – 475, Tallinn, June 2011.
[15] H. Boudjedir, F. Yacef, O. Bouhali, N. Rizoug, “Adaptive neural network for a Quadrotor unmanned aerial vehicle”, International Journal in Foundations of Computer Science and Technology, Vol.2, pp.1–13, July 2012.
[16] B.Y. Lee, H.I. Lee, Min-Jea Tahk, “Analysis of adaptive control using on-line neural networks for a quadrotor uav”, Proceeding of the IEEE/ICCAS, pp.1840–1844, Korea, Oct. 2013.
[17] M. Mahfouz, M. Ashry, G. Elnashar, “Design and control of quad-rotor helicopters based on adaptive neuro-fuzzy inference system”, International Journal of Engineering Research Technology (IJERT), Vol. 2, pp. 479–485, Dec. 2013.
[18] White, David Ashley, Donald A. Sofge, eds. Handbook of Intelligent Control: Neural, Fuzzy, and Adaptative Approaches. Van Nostrand Reinhold Company, 1992.
[19] Y. Amir, V. Abbass, “Modeling of quadrotor helicopter dynamics”, Proceeding of the IEEE/ICSMA, pp. 100-105, Gyeonggi-do, April 2008.
[20] X. Gong, Z.C. Hu, C.J. Zhao, Y. Bai, Y.T. Tian, “Adaptive back stepping sliding mode trajectory tracking control for a quad-rotor”, IEEE International Journal of Automation and Computing, Vol. 9, pp. 555–560, Oct. 2012.
[21] H. Voos, “Nonlinear state-dependent riccati equation control of a quadrotor uav”, Proceeding of the IEEE/ICCACACSD, pp.2547–2552, Munich, Germany, Oct. 2006.
[22] A.A. Zamani, S.M. Kargar-Dehnavi, ”Compensation of actuator’s saturation by using fuzzy logic and imperialist competitive algorithm in a system with PID controller”, Journal of Intelligent Procedures in Electrical Technology (JIPET), Vol. 3, No. 11, pp. 21-26, Summer 2013. (in Persian)
[23] M. Lotfi-Forushani, B. Karimi, Gh. Shahgholian, “Optimal PID controller tuning for multivariable aircraft longitudinal autopilot based on particle swarm optimization algorithm”, Journal of Intelligent Procedures in Electrical Technology (JIPET), Vol. 3, No. 9, pp. 41-50, Spring 2012. (in Persian)
[24] M. Safaei, S. Hosseinia, M. Hosseini-Toodeshki, “A general method for designing fractional order PID controller”, Journal of Intelligent Procedures in Electrical Technology (JIPET), Vol. 3, No. 12, pp. 25-34, Winter 2013. (in Persian)
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