Journal Article

An improved dynamic model identification method for small unmanned helicopter

Liu, Shuyu, Zhou, Jian, Liu, Xinyu, Lu, Jian

2024

Overview

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.

• Publisher: Emerald Publishing Limited,Emerald Group Publishing Limited
• Subject: Accuracy / Adaptive algorithms / Ant colony optimization / Curve fitting / Design parameters / Dynamic characteristics / Dynamic models / Flight tests / Frequency domain analysis / Genetic algorithms / Global optimization / Helicopters / Identification / Identification methods / Laplace transforms / Mathematical models / Model accuracy / Optimization / Parameter identification / Surveillance / System identification / Unmanned aerial vehicles / Unmanned helicopters