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. 2018 Aug 13;376(2126):20170248.
doi: 10.1098/rsta.2017.0248.

A wavelet neural control scheme for a quadrotor unmanned aerial vehicle

F Jurado  1 , S Lopez  2
Affiliations

A wavelet neural control scheme for a quadrotor unmanned aerial vehicle

F Jurado et al. Philos Trans A Math Phys Eng Sci. .

Abstract

Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time-frequency plane. The Gaussian, the Mexican hat and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using the continuous wavelet transform. Continuous wavelets are favoured when high temporal and spectral resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called a recurrent Morlet wavelet neural network, is proposed in order to achieve a better identification of the behaviour of dynamic systems. The effectiveness of our proposal is explored through the design of a decentralized neural backstepping control scheme for a quadrotor unmanned aerial vehicle. The performance of the overall neural identification and control scheme is verified via simulation and real-time results.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

Keywords: Morlet wavelet; backstepping control; quadrotor; recurrent wavelet neural network.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1.
Figure 1.
Decentralized Morlet wavelet neural control scheme. (Online version in colour.)
Figure 2.
Figure 2.
RMWNN controller. (Online version in colour.)
Figure 3.
Figure 3.
The Unmanned Vehicle Systems (UVS) Laboratory from Quanser provides a turn-key, integrated environment for exploring a wide range of advanced research applications. Integrating the Quanser QBall 2 system with additional QBall and QBot ground robot units allows researchers to build a flexible, open architecture, multi-agent platform for research. Image used with permission from Quanser.
Figure 4.
Figure 4.
(a) Circular trajectory tracking performed by the decentralized neural controllers and (b) dynamics of the attitude angles.
Figure 5.
Figure 5.
(a) Square-shaped trajectory tracking performed by the decentralized neural controllers and (b) dynamics of the attitude angles.
Figure 6.
Figure 6.
(a) Square-shaped trajectory tracking performed by the decentralized RMWNN controller when considering the presence of disturbance, measurement noise and uncertainty and (b) dynamics of the attitude angles.
Figure 7.
Figure 7.
Real-time trajectory tracking performed by the decentralized RMWNN controller.
Figure 8.
Figure 8.
(a) Real-time translational displacement by the decentralized RMWNN controller in the (x, z)-plane; (b) real-time translational displacement by the decentralized RMWNN controller in the (y, z)-plane.
Figure 9.
Figure 9.
(a) Error signal for the x-coordinate and (b) error signal for the y-coordinate.
Figure 10.
Figure 10.
(a) Error signal for the z-coordinate and (b) control signal for the vertical thrust.
Figure 11.
Figure 11.
(a) Error signal for the angular coordinate ψ and (b) control signal for the yaw movement.
Figure 12.
Figure 12.
(a) Error signal for the angular coordinate θ and (b) control signal for the pitch movement.
Figure 13.
Figure 13.
(a) Error signal for the angular coordinate ϕ and (b) control signal for the roll movement.
See this image and copyright information in PMC

References

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