پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 405 |
| تعداد مقالات | 5,424 |
| تعداد مشاهده مقاله | 5,542,635 |
| تعداد دریافت فایل اصل مقاله | 5,026,950 |
طراحی کنترل‌کننده غیرخطی پهپاد چهارروتور به کمک روش ترکیبی گرادیان ازدحام ذرات | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 6، دوره 50، شماره 5، آذر و دی 1397، صفحه 989-998 اصل مقاله (1.07 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2016.859 | ||
| نویسندگان | ||
| حامد شهبازی* ؛ وحید تیکنی | ||
| دانشکده مهندسی مکاترونیک، دانشگاه اصفهان، اصفهان، ایران | ||
| چکیده | ||
| در این مقاله با ترکیب ایده هایی از یادگیری تقویتی گرادیان سیاست و روش ازدحام ذرات یک روش ترکیبی بهینه سازی برای کنترل یک سامانه پیچیده غیرخطی ارائه شده است که کاربردهای فراوانی در جهان واقعی خواهد داشت. این سامانه ترکیبی بر روی یک پرنده هدایت پذیر از دور چهارروتور نصب شده است که با هدف کنترل جهت‌گیری و موقعیت پهپاد عمل می‌کند. در این روش با گرفتن ایده از روش‌های تقویتی، گرادیان سیاست در کنترل‌کننده مشتق گیر تناسبی یک چهارروتور محاسبه می‌شود و در روابط بهینه سازی ازدحام ذرات وارد می‌گردد تا بهینه‌سازی علاوه بر فاکتورهای لحاظ شده در روش‌های ازدحامی در جهت گرادیان سیاست کنترلی نیز انجام شود. برای انجام بهینه سازی ورودی‌های کنترلی و مشخصه های سامانه ازجمله: زمان پاس خدهی سامانه، خطای ماندگار سامانه، فراجهش و زمان نشست سامانه در تابع هزینه برای بهینه سازی منظور شده اند. روش ارائه شده بر روی بستر عملی آزمون چهارروتور پیاده سازی و با تعدادی از روش‌های مرسوم مقایسه شده است. | ||
| کلیدواژه‌ها | ||
| چهارروتور؛ کنترل بهینه؛ گرادیان سیاست؛ بهینه‌سازی ازدحام ذرات | ||
| عنوان مقاله [English] | ||
| Design of a Nonlinear Controller on Quadrotor Drone Using Combined Method of Gradient Particle Swarm Optimization | ||
| نویسندگان [English] | ||
| H. Shahbazi؛ V. Tikani | ||
| Department of Mechanical Engineering, University of Isfahan, Isfahan, Iran | ||
| چکیده [English] | ||
| In the paper a new method of optimal control in presented which is composed of policy gradient reinforcement learning and particle swarm optimization. This method has a lot of applications in the real world. The combined method is implemented on a quadrotor drone to control attitude and position of the drone. Inspired from reinforcement methods, the gradient of the policy is computed for a proportional-integral-derivative controller and used in particle swarm optimization to be used in optimization process in addition to the other factors. To study the performance of Optimal proportional-integral-derivative controller on attitude control of the system, a quadrotor is fixed to the design a test stand. The system consists of an accelerometer and a gyroscope sensors and a microcontroller which is used to design fuzzy proportional-integral-derivative attitude controller for the quadrotor. Considering that the experimental data has lots of errors and noises, Kalman filter is used to reduce the noises. Finally using Kalman filter leads to better estimation of the quadrotor angles and the optimized proportional-integral-derivative controller performs the desired motions successfully. The presented method is implemented and tested on the quadrotor test bench and compared with some old methods. To check the robustness of the proportional-integral-derivative controller to the external disturbances, random disturbances are applied to the quadrotor. The controller stabilized the quadrotor rapidly even with disturbance is applied. | ||
| کلیدواژه‌ها [English] | ||
| Quadrotor, Optimal control, Policy Gradient, Particle Swarm Optimization | ||
|
سایر فایل های مرتبط با مقاله
|
||
| مراجع | ||
|
[1] A. Gessow, G.C. Myers, Aerodynamics of the Helicopter, Macmillan New York, 1952. [2] J.J.P. Leishman, New, Principles of Helicopter Aerodynamics, CambridgeUniv, (2000). [3] M.J. Hirschberg, The American Helicopter, (2000). [4] P. Castillo, R. Lozano, A.E. Dzul, Modelling and control of mini-flying machines, Physica-Verlag, 2006. [5] H. Chao, Y. Cao, Y.J.I.J.o.C. Chen, Automation, Systems, Autopilots for small unmanned aerial vehicles: a survey, 8(1) (2010) 36-44. [6] D.-J. Lee, I. Kaminer, V. Dobrokhodov, K.J.I.J.o.C. Jones, Automation, Systems, Autonomous feature following for visual surveillance using a small unmanned aerial vehicle with gimbaled camera system, 8(5) (2010) 957-966. [7] D.-i. Han, J.-h. Kim, C.-o. Min, S.-j. Jo, J.-h. Kim, D.-w.J.I.J.o.C. Lee, Automation, Systems, Development of Unmanned Aerial Vehicle (UAV) system with waypoint tracking and vision-based reconnaissance, 8(5) (2010) 1091-1099. [8] E. Altug, J.P. Ostrowski, C.J. Taylor, Quadrotor control using dual camera visual feedback, in: Robotics and Automation, 2003. Proceedings. ICRA'03. IEEE International Conference on, IEEE, 2003, pp. 4294-4299. [9] D. Suter, T. Hamel, R. Mahony, Visual servo control using homography estimation for the stabilization of an x4-flyer, in: Decision and Control, 2002, Proceedings of the 41st IEEE Conference on, IEEE, 2002, pp. 2872-2877. [10] J. Dunfied, M. Tarbouchi, G. Labonte, Neural network based control of a four rotor helicopter, in: Industrial Technology, 2004. IEEE ICIT'04. 2004 IEEE International Conference on, IEEE, 2004, pp. 1543-1548. [11] M.G. Earl, R. D'Andrea, Real-time attitude estimation techniques applied to a four rotor helicopter, in: Decision and Control, 2004. CDC. 43rd IEEE Conference on, IEEE, 2004, pp. 3956-3961. [12] D. Lee, H.J. Kim, S.J.I.J.o.c. Sastry, Automation, systems, Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter, 7(3) (2009) 419-428. [13] B. Erginer, E.J.I.J.o.C. Altuğ, Automation, Systems, Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle, 10(1) (2012) 61-70. [14] A. Sharma, A.J.I.J.E.E.C.E. Barve, Controlling of Quad-rotor UAV using pId controller and Fuzzy logic controller, 1(2) (2012) 38-41. [15] L.-X. Wang, A course in fuzzy systems and control: Desing of Fuzzy Systems from Input-Output Data, in, Prentice-Hall, Upper Saddle River, NJ, 1997. [16] C.-C.J.I.T.o.s. Lee, man,, cybernetics, Fuzzy logic in control systems: fuzzy logic controller. I, 20(2) (1990) 404-418. [17] L. Reznik, Fuzzy controllers handbook: how to design them, how they work, Elsevier, 1997. [18] E.H.-K. Fung, Y.-K. Wong, Y. Ma, C.-W.M. Yuen, W.-K.J.I.J.o.C. Wong, Automation, Systems, Smart hanger dynamic modeling and fuzzy controller design, 9(4) (2011) 691. [19] A. Hafaifa, F. Laaouad, K.J.I.J.o.C. Laroussi, Automation, Systems, A numerical structural approach to surge detection and isolation in compression systems using fuzzy logic controller, 9(1) (2011) 69-79. [20] M. Sugeno, I. Hirano, S. Nakamura, S. Kotsu, Development of an intelligent unmanned helicopter, in: Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int, IEEE, 1995, pp. 33-34. [21] B. Kadmiry, D. Driankov, Fuzzy control of an autonomous helicopter, in: IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, IEEE, 2001, pp. 2797-2802. [22] C. Cavalcante, J. Cardoso, J.J. Ramos, O.R. Neves, Design and tuning of a helicopter fuzzy controller, in: Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int, IEEE, 1995, pp. 1549-1554. [23] N.I. Vitzilaios, N.C.J.J.o.I. Tsourveloudis, R. Systems, An experimental test bed for small unmanned helicopters, 54(5) (2009) 769-794. [24] R. Garcia, K.P. Valavanis, The implementation of an autonomous helicopter testbed, in: Unmanned Aircraft Systems, Springer, 2008, pp. 423-454. [25] Y.J.H.R.-W. Wu, Μάρτιοσ, Development and Implementation of a Control System for a quadrotor UAV, (2009). [26] R.J.U.P.D. Barclay, University of Sheffield, United Kingdom, A Generic Simulator for Quad-Rotor Unmanned Aerial Vehicles, (2005). [27] M.B. Srikanth, Z.T. Dydek, A.M. Annaswamy, E. Lavretsky, A robust environment for simulation and testing of adaptive control for mini-UAVs, in: American Control Conference, 2009. ACC'09., IEEE, 2009, pp. 5398-5403. [28] Z.-Y. Zhao, M. Tomizuka, S.J.I.t.o.s. Isaka, man,, cybernetics, Fuzzy gain scheduling of PID controllers, 23(5) (1993) 1392-1398. [29] J.G.J.t.A. Ziegler, Optimum settings for automatic controllers, 65 (1943) 433-444. [30] S.J.F.P.C.J.J.o.B.U.o.A. Kaiyuan, Astronautics, Attitude control of quadrotor aircraft via nonlinear PID [J], 9 (2011) 003. [31] S. Bouabdallah, Design and control of quadrotors with application to autonomous flying, Epfl, 2007. [32] H. Bolandi, M. Rezaei, R. Mohsenipour, H. Nemati, S.M.J.I.C. Smailzadeh, Automation, Attitude control of a quadrotor with optimized PID controller, 4(03) (2013) 335. [33] A.L. Salih, M. Moghavvemi, H.A. Mohamed, K.S.J.S.r. Gaeid, essays, Flight PID controller design for a UAV quadrotor, 5(23) (2010) 3660-3667. [34] G. Endo, J. Morimoto, T. Matsubara, J. Nakanishi, G.J.T.I.J.o.R.R. Cheng, Learning CPG-based biped locomotion with a policy gradient method: Application to a humanoid robot, 27(2) (2008) 213-228. [35] T. Matsubara, J. Morimoto, J. Nakanishi, M.-a. Sato, K.J.R. Doya, A. Systems, Learning CPG-based biped locomotion with a policy gradient method, 54(11) (2006) 911-920. [36] Y. Nakamura, T. Mori, M.-a. Sato, S.J.N.N. Ishii, Reinforcement learning for a biped robot based on a CPG-actor-critic method, 20(6) (2007) 723-735. [37] S. Bhatnagar, R.S. Sutton, M. Ghavamzadeh, M.J.A. Lee, Natural actor–critic algorithms, 45(11) (2009) 2471-2482. [38] S. Bhatnagar, M. Ghavamzadeh, M. Lee, R.S. Sutton, Incremental natural actor-critic algorithms, in: Advances in neural information processing systems, 2008, pp. 105-112. [39] F.S. Melo, M. Lopes, Fitted natural actor-critic: A new algorithm for continuous state-action MDPs, in: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2008, pp. 66-81. [40] T. Degris, P.M. Pilarski, R.S. Sutton, Model-free reinforcement learning with continuous action in practice, in: American Control Conference (ACC), 2012, IEEE, 2012, pp. 2177-2182. [41] M.-S. Leu, M.-F.J.A.S.C. Yeh, Grey particle swarm optimization, 12(9) (2012) 2985-2996. [42] Y. Liu, Z. Qin, X. He, Supervisor-student model in particle swarm optimization, in: Evolutionary Computation, 2004. CEC2004. Congress on, IEEE, 2004, pp. 542-547. | ||
|
آمار تعداد مشاهده مقاله: 1,318 تعداد دریافت فایل اصل مقاله: 2,549 |
||
