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Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV
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 Title & Authors
Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV
Chen, Xiang-Jian; Li, Di;
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 Abstract
This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-II fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-II fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-II fuzzy neural network based adaptive sliding mode controller could guarantee the Eight-Rotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-I fuzzy neural network based sliding mode controller.
 Keywords
Interval type-II fuzzy neural network;sliding mode controller;Eight-Rotor MAV;
 Language
English
 Cited by
1.
A new robust fuzzy method for unmanned flying vehicle control, Journal of Central South University, 2015, 22, 6, 2166  crossref(new windwow)
2.
H infinity control design for Eight-Rotor MAV attitude system based on identification by interval type II fuzzy neural network, International Journal of Aeronautical and Space Sciences, 2016, 17, 2, 195  crossref(new windwow)
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