Design and Implementation of Fuzzy Controller for Estimating the Attitude of Unmanned Aerial Vehicles

Roopashree, S.; Deepika, K. M.; Bhat, Shubha

doi:10.1007/978-3-642-32573-1_43

Cited by 2 publications

(1 citation statement)

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“…This framework was useful in evolving a sophisticated set of rules for the controller of a UAV, which aimed to deal with uncertainty in both its internal state and external environment. Roopashree et al (2012) described the design of compact, accurate and inexpensive Fuzzy Logic Controllers (FLCs) and Fuzzy Inference Systems (FISs), which estimated the attitude of UAV. Sabo and Cohen (2012) presented a methodology for two-dimensional motion planning of a UAV using fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Neural network fuzzy control for enhancing the stability performance of quad-rotor helicopter

Wang

Ding

Cao

et al. 2017

Transactions of the Institute of Measurement and Control

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Recently, the quad-rotor helicopter has gained increasing attention owing to its very good flexibility, its ability to execute various flight missions even in harsh environments. The quad-rotor helicopter can implement different fight attitudes, which is attributed to the effective control of the motor speed about four propellers. In order to make the quad-rotor helicopter can better finish flight mission, the performance of flight stability then becomes particularly important. A neural network fuzzy control algorithm is proposed in this paper so as to guarantee the stability performance of the quad-rotor helicopter. The proposed algorithm is based on the neural network, which keeps the self-organization and self-learning ability, besides this, it utilizes the strong impression ability of constitutive knowledge as to the fuzzy logic. The proposed control scheme aims to implement good abilities such as describing qualitative knowledge, strong learning mechanism and direct processing about quantitative data of the quad-rotor helicopter. In the practical flight process of the quad-rotor helicopter, while the deviation of position and attitude information become larger, fuzzy control is adopted so as to shorten the overshoot and adjustment time. On the other hand, if the deviation of position and attitude become relatively smaller, neural network PID control will be used so as to reduce the error. Experimental results show that the proposed neural network fuzzy control algorithm exhibits good performance in the flight process of the quad-rotor helicopter.

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Section: Introductionmentioning

confidence: 99%