Purpose

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

Design/methodology/approach

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

Findings

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

Originality/value

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

中文翻译：

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一种改进的小型无人直升机动力学模型辨识方法

目的

本文的目的是提出一种结合自适应蚁群优化算法和Levy方法的改进的小型无人直升机系统辨识方法，解决小型无人直升机频率下低频域曲线拟合导致的模型预测精度低的问题。域参数识别方法。

设计/方法论/途径

该方法利用Levy方法获得拟合模型的初始参数，利用自适应蚁群算法的全局优化特性和避免“早熟”现象的优点对初始参数进行优化，最终通过横向通道和纵向通道下的计算优化动力学模型。

发现

该算法通过飞行试验数据进行了验证。验证结果表明，建立的动力学模型辨识精度较高，能够准确反映小型无人直升机飞行的动力学特性。

原创性/价值

本文提出了一种新颖且改进的小型无人直升机频域识别方法。与传统方法相比，该方法提高了识别精度，减少了识别误差。