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首页> 外文期刊>International Journal of Intelligent Systems Technologies and Applications >Recursive Gauss-Newton based training algorithm for neural network modelling of an unmanned rotorcraft dynamics
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Recursive Gauss-Newton based training algorithm for neural network modelling of an unmanned rotorcraft dynamics

机译：基于递归高斯-牛顿训练算法的无人旋翼飞机动力学神经网络建模

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摘要

The ability to model the time varying dynamics of an unmanned rotorcraft is an important aspect in the development of adaptive flight controller. This paper presents a recursive Gauss-Newton based training algorithm to model the attitude dynamics of a small scale rotorcraft based unmanned aerial system using the neural network (NN) modelling approach. It focuses on selection of optimised network for recursive algorithm that offers good generalisation performance with the aid of the cross validation method proposed. The recursive method is then compared with the off-line Levenberg-Marquardt (LM) training method to evaluate the generalisation performance and adaptability of the model. The results indicate that the recursive Gauss-Newton (rGN) method gives slightly lower generalisation performance compared with its off-line counterpart but adapts well to the dynamic changes that occur during flight. The proposed recursive algorithm was found effective in representing helicopter dynamics with acceptable accuracy within the available computational time constraint.
机译：对无人旋翼飞机的时变动力学建模的能力是自适应飞行控制器发展的重要方面。本文提出了一种基于递归高斯-牛顿算法的训练算法，可使用神经网络（NN）建模方法对小型旋翼飞机无人航空系统的姿态动力学进行建模。它着重于为递归算法选择最佳网络，该网络借助所提出的交叉验证方法可提供良好的泛化性能。然后将递归方法与离线Levenberg-Marquardt（LM）训练方法进行比较，以评估模型的泛化性能和适应性。结果表明，与脱机高斯-牛顿（rGN）方法相比，其递归高斯-牛顿（rGN）方法的泛化性能略低，但能够很好地适应飞行过程中发生的动态变化。发现提出的递归算法在可用的计算时间限制内以可接受的精度有效表示直升机动力学。

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