Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107494
Title: Vision-based adaptive neural positioning control of quadrotor aerial robot
Authors: Zhang, Yun
Lyu, Yi
Lai, Guanyu
Chen, Ci
Keywords: Neural Networks
Engineering::Electrical and electronic engineering
Adaptive Control
Issue Date: 2019
Source: Lyu, Y., Lai, G., Chen, C., & Zhang, Y. (2019). Vision-based adaptive neural positioning control of quadrotor aerial robot. IEEE Access, 7, 75018-75031. doi:10.1109/ACCESS.2019.2920716
Series/Report no.: IEEE Access
Abstract: In this paper, a new vision-based adaptive control algorithm is proposed for the positioning of a quadrotor aerial robot (QAR) with an onboard pin-hole camera. First, the transformation between the position tracking error and image projection error is constructed through the spherical projection method, and then the regulation of the position error is achieved indirectly by stabilizing the image projection error. To overcome the challenge that the dynamics of QAR is physically underactuated, a backstepping-based approach that synthesizes the Lipschitz condition and natural saturation of the inverse tangent function is proposed. In the proposed adaptive controller, an optimized adaptive neural network (NN) means is designed, where only the square of the NN weight matrix's maximum singular value, not the weight matrix itself, is estimated. Moreover, to facilitate practical application, a novel inertial matrix estimator is introduced in the tuning laws, so that the accurate QAR rotation inertial information is not required. By Lyapunov theory, it is proved that the image projection error converges to an adjustable region of zero asymptotically. The effectiveness of the proposed algorithm has been confirmed by the experimental results.
URI: https://hdl.handle.net/10356/107494
http://hdl.handle.net/10220/49708
DOI: http://dx.doi.org/10.1109/ACCESS.2019.2920716
Rights: © 2019 IEEE. Articles accepted before 12 June 2019 were published under a CC BY 3.0 or the IEEE Open Access Publishing Agreement license. Questions about copyright policies or reuse rights may be directed to the IEEE Intellectual Property Rights Office at +1-732-562-3966 or copyrights@ieee.org.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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