Intelligent adaptive trajectory tracking control using fuzzy basis function networks for an autonomous small-scale helicopter

This paper presents an intelligent adaptive trajectory tracking controller using fuzzy basis function networks (FBFN) for an autonomous small-scale helicopter. With the on-line FBFN approximation to the vehicle mass and the coupling effect between the force and the moments, the intelligent adaptive controller is systematically synthesized using backstepping technique. This controller is shown to achieve the semi-global ultimate boundedness of the closed-loop helicopter dynamics and accommodate agile flight maneuvers in trajectory tracking. The effectiveness and merit of the proposed method are exemplified by performing one nonlinear simulation and by performance comparison with a well-known controller.

[1]  Chuan-Kai Lin,et al.  Fuzzy-Basis-Function-Network-Based $H_\infty$ Tracking Control for Robotic Manipulators Using Only Position Feedback , 2009, IEEE Transactions on Fuzzy Systems.

[2]  Chuan-Kai Lin,et al.  Adaptive critic autopilot design of Bank-to-turn missiles using fuzzy basis function networks , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  S. Żak Systems and control , 2002 .

[4]  Jong Shik Kim,et al.  Development of a novel dynamic friction model and precise tracking control using adaptive back-stepping sliding mode controller , 2006 .

[5]  Won-Ho Kim,et al.  Adaptive robust fuzzy control and implementation for path tracking of a mobile robot , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[6]  Yeong-Chan Chang,et al.  Intelligent Robust Tracking Control for a Class of Uncertain Strict-Feedback Nonlinear Systems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Chih-Min Lin,et al.  Recurrent-neural-network-based adaptive-backstepping control for induction servomotors , 2005, IEEE Transactions on Industrial Electronics.

[8]  Ching-Chih Tsai,et al.  Nonlinear adaptive aggressive control using recurrent neural networks for a small scale helicopter , 2010 .

[9]  C. M. Vélez,et al.  Rapid software prototyping for real-time simulation and control of a mini-helicopter robot , 2006 .

[10]  F. G. Shinskey,et al.  2.19 Nonlinear and Adaptive Control , 2008 .

[11]  Ching-Chih Tsai,et al.  Improved nonlinear trajectory tracking using RBFNN for a robotic helicopter , 2010 .

[12]  Sung-Suk Kim,et al.  Development of Quantum-Based Adaptive Neuro-Fuzzy Networks , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[14]  Narasimhan Sundararajan,et al.  Adaptive back-stepping neural controller for reconfigurable flight control systems , 2006, IEEE Transactions on Control Systems Technology.

[15]  Faa-Jeng Lin,et al.  Intelligent Adaptive Backstepping Control System for Magnetic Levitation Apparatus , 2007, IEEE Transactions on Magnetics.

[16]  Eric A. Wan,et al.  SDRE CONTROL WITH NONLINEAR FEEDFORWARD COMPENSATION FOR A SMALL UNMANNED HELICOPTER , 2003 .