State predictor-based deep model reference adaptive control for quadrotor trajectory tracking

Zhekun Cheng, Jueying Yang, Yi Sun, Liangyu Zhao*, Lin Zhao

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

The application of quadrotor unmanned aerial vehicle (UAV) swarms has attracted considerable attention in recent years, but the dense formations also pose new challenges to controlling quadrotors. In these cases, quadrotors frequently encounter matched and unmatched disturbances from fellow swarm members. To achieve precise tracking of the desired trajectory with optimal accuracy, a state predictor-based deep model reference adaptive control (PDMRAC) framework is proposed. Owing to the powerful feature extraction capability of deep neural networks (DNN) and the enhancement of transient characteristics of the system by the state predictor, the control framework's performance in approximating unstructured uncertainty is improved. The controller designed based on the nonlinear model compensates for the matched uncertainty and reacts to the unmatched uncertainty to reduce the tracking error. Moreover, the controller maintains accurate tracking performance for unseen trajectories and uncertainties when well-trained DNNs are employed as frozen weight networks. Numerical simulations are conducted to evaluate trajectory tracking performance in an environment featuring time-varying disturbances, and the results demonstrate the effectiveness of the proposed method.

源语言英语
文章编号109868
期刊Aerospace Science and Technology
157
DOI
出版状态已出版 - 2月 2025

指纹

探究 'State predictor-based deep model reference adaptive control for quadrotor trajectory tracking' 的科研主题。它们共同构成独一无二的指纹。

引用此

Cheng, Z., Yang, J., Sun, Y., Zhao, L., & Zhao, L. (2025). State predictor-based deep model reference adaptive control for quadrotor trajectory tracking. Aerospace Science and Technology, 157, 文章 109868. https://doi.org/10.1016/j.ast.2024.109868