Journal
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
Volume 9, Issue 6, Pages 1154-1163Publisher
INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
DOI: 10.1007/s12555-011-0617-1
Keywords
Dynamical modeling of eight-rotor MAV; Lyapunov stability theorem; neuro-fuzzy adaptive controller; PID; Type-II fuzzy nerual network
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Funding
- National Natural Science Foundation of China [NSFC 50905174]
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This paper focuses on modeling and intelligent control of the new Eight-Rotor MAV which is used to solve the problem of low coefficient proportion between lift and gravity for Quadrotor MAV. The dynamical and kinematical modeling for the Eight-Rotor MAV was developed which has never been proposed before. Based on the achieved dynamic modeling, two types of controller were presented. One type, a PID controller is derived in a conventional way with simplified dynamics and turns out to be quite sensitive to sensor noise as well as external perturbation. The second type controller is the Neuro-Fuzzy adaptive controller which is composed of two type-II fuzzy neural networks (T-IIFNNs) and one PD controller: The PD controller is adopted to control the attitude, one of the T-IIFNNs is designed to learn the inverse model of Eight-Rotor MAV on-line, the other one is the copy of the former one to compensate for model errors and external disturbances, both structure and parameters of T-IIFNNs are tuned on-line at the same time, and then the stability of the Eight-Rotor MAV closed-loop control system is proved using Lyapunov stability theory. Finally, the validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of Neuro-Fuzzy adaptive controller performs very well under sensor noise and external disturbances, and has more superiority than traditional PID controller.
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