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A. Mokhtari, A. Benallegue, Y. Orlov (2006)
Exact linearization and sliding Mode observer for a quadrotor Unmanned Aerial VehicleInt. J. Robotics Autom., 21
S. Bouabdallah, A. Noth, R. Siegwart (2004)
PID vs LQ control techniques applied to an indoor micro quadrotor2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 3
Chi-Hsu Wang, Chun-Sheng Cheng, Tsu-Tian Lee (2003)
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34
C. Yang, Wen-Hsiung Liu (2003)
Nonlinear H/sup /spl infin// decoupling hover control of helicopter with parameter uncertaintiesProceedings of the 2003 American Control Conference, 2003., 4
Cheul Hwang, F. Rhee (2007)
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-MeansIEEE Transactions on Fuzzy Systems, 15
P. Castillo, R. Lozano, A. Dzul
Modeling and Control of Mini Flying Machines
T. Kuremoto, M. Obayashi, Kunikazu Kobayashi (2009)
Adaptive swarm behavior acquisition by a neuro-fuzzy system and reinforcement learning algorithmInt. J. Intell. Comput. Cybern., 2
P. Castillo, A. Dzul, R. Lozano (2004)
Real-time stabilization and tracking of a four-rotor mini rotorcraftIEEE Transactions on Control Systems Technology, 12
I. Kanellakopoulos, P. Kokotovic, A. Morse (1991)
Systematic Design of Adaptive Controllers for Feedback Linearizable Systems1991 American Control Conference
A. Mokhtari, A. Benallegue, B. Daachi
Robust feedback linearization and GH ∞ controller for a quadrotor unmanned aerial vehicle
P. Castillo, R. Lozano, A. Dzul
Stabilization of a mini rotorcraft having four rotors
J. Jang (1993)
ANFIS: adaptive-network-based fuzzy inference systemIEEE Trans. Syst. Man Cybern., 23
T. Madani, A. Benallegue (2006)
Backstepping Control for a Quadrotor Helicopter2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
P. Castillo, R. Lozano, A. Dzul (2005)
Stabilization of a mini rotorcraft with four rotorsIEEE Control Systems, 25
Xu Xiao-lai (2010)
Training self-organizing fuzzy neural networks with unscented Kalman filterSystems engineering and electronics
M. Er, Tien Tan, S. Loh (2004)
Control of a mobile robot using generalized dynamic fuzzy neural networksMicroprocess. Microsystems, 28
A. Mokhtaril, A. Benallegue, B. Daachi
2005 Ieee/rsj International Conference on Intelligent Robots and Systems Robust Feedback Linearization and Gh(f Controller for a Quadrotor Unmanned Aerial Vehicle
A. Calise, B.S. Kim, J. Leitner, J. Prasad (1994)
Helicopter adaptive flight control using neural networksProceedings of 1994 33rd IEEE Conference on Decision and Control, 4
R. Mahony, T. Hamel (2004)
Robust trajectory tracking for a scale model autonomous helicopterInternational Journal of Robust and Nonlinear Control, 14
M. Er, C. Low, Khuan Nah, M. Lim, Shee Ng (2002)
Real-time implementation of a dynamic fuzzy neural networks controller for a SCARAMicroprocess. Microsystems, 26
Liu Zhisheng, Liu Junshan, Feng Fan, Zhao Xin (2008)
Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluationJournal of Systems Engineering and Electronics, 19
S. Bouabdallah, A. Noth, R. Siegwart
PID vs LQ control techniques applied to an weight augmentation high energy consumption indoor micro quadrotor
Ching-Hung Lee, Yu-Ching Lin, Wei-Yu Lai (2003)
Systems identification using type-2 fuzzy neural network (type-2 FNN) systemsProceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694), 3
X. Gong, Yue Bai, Z. Hou, Changjun Zhao, Yantao Tian, Q. Sun (2012)
Backstepping sliding mode tracking control of quad-rotor under input saturationInt. J. Intell. Comput. Cybern., 5
R. Enns, J. Si (2000)
Helicopter flight control design using a learning control approachProceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2
C.D. Yang, W.H. Liu
Nonlinear H ∞ decoupling hover control of helicopter with parameter uncertainties
A. Das, F. Lewis, K. Subbarao (2009)
Backstepping Approach for Controlling a Quadrotor Using Lagrange Form DynamicsJournal of Intelligent and Robotic Systems, 56
Q. Wei, X.H. Wang, Z.Y. Zhou, S.S. Xiong
Guidance and implement of micro system based on GPS
Purpose – Quadrotor micro aerial vehicle (MAV) is nonlinear and under actuated plant, and it is difficult to obtain an accurate mathematical model for quadrotor MAV due to uncertainties. The purpose of this paper is to propose one robust control strategy for quadrotor MAV to accommodate system uncertainties, variations, and external disturbances. Design/methodology/approach – The robust control strategy is composed of two self‐organizing interval type‐II fuzzy neural networks (SOIT‐IIFNNs) and one PD controller: the PD controller is adopted to control the attitude and position; one of the SOIT‐IIFNNs is designed to learn the inverse model of quadrotor MAV online; the other SOIT‐IIFNNs is the copy of the former one to compensate for model errors, system uncertainties and external disturbances, both structure and parameters of SOIT‐IIFNNs are tuned online at the same time, and then the stability of the resulting quadrotor MAV closed‐loop control system is proved using Lyapunov stability theory. Findings – The validity of the proposed control method has been verified through real‐time experiments. The experimental results show that the performance of SOIT‐IIFNNs is significantly improved compared with Backstepping‐based controller. Practical implications – This approach has been used in quadrotor MAV, the controller works well, and it could guarantee quadrotor MAV control system with good performances under uncertainties, variations, and external disturbances. Originality/value – The proposed SOIT‐IIFNNs controller is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the overall closed‐loop control system is globally stable, demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the quadrotor MAV control system based on SOIT‐IIFNNs controller can achieve favorable tracking performance.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Aug 23, 2011
Keywords: Control technology; Microcontrollers; Aircraft components; Rotors
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