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Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

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Abstract

The mathematical modeling of a small unmanned helicopter (SUH) with multivariable, highly nonlinear and complex dynamic characteristics is considered. This paper presents a modeling method for SUHs based on a particle swarm optimization least squares support vector machine (PSO-LSSVM) with a hybrid kernel function. The proposed method is based on a least square support vector machine and uses linear weighting of the polynomial kernel function (POLY) and Gaussian kernel function (RBF) to form a hybrid kernel function, and uses a particle swarm optimization algorithm to search for the optimal parameters. Finally, a mathematical model of the longitudinal and lateral passages of a SUH is established. According to the flight test data, the longitudinal and lateral channel models are trained and verified in the hover and low-speed forward flight states of a SUH. The experimental and comparison results demonstrate that the model established via this method has higher prediction accuracy and more accurate prediction results than a model established using a least squares support vector machine with a single kernel function. The identification accuracy of the SUH model is improved effectively.

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Author notes

  1. Weixin Wang, Jian Lu and Lingzhe Liu have contributed equally to this work.

Authors and Affiliations

  1. School of Electronics and Information, Xi’an Polytechnic University, Xi’an, 710048, Shaanxi Province, China

    Jian Zhou, Weixin Wang, Jian Lu & Lingzhe Liu

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  1. Jian Zhou
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  2. Weixin Wang
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  3. Jian Lu
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Contributions

Jian Zhou and Weixin Wang wrote the manuscript text, Jian Zhou prepared Figs. 1, 2 and 3, and Jian Lu and Lingzhe Liu prepared Figs. 4, 5, 6, 7, 8 and 9. All authors reviewed the manuscript.

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Correspondence to Jian Zhou.

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Zhou, J., Wang, W., Lu, J. et al. Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM. J Supercomput 79, 13889–13906 (2023). https://doi.org/10.1007/s11227-023-05211-5

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