固体火箭技术2025，Vol.48Issue(5)：799-806,8.DOI:10.7673/j.issn.1006-2793.2025.05.019
基于深度神经网络与状态预测器的无人飞行器自适应控制
Adaptive control of unmanned aerial vehicle based on deep neural network and state predictor
摘要
Abstract
In swarm flight scenarios,the widespread presence of unstructured uncertainties can adversely affect the control per-formance of unmanned aerial vehicles and even pose flight safety risks.An adaptive control method based on deep neural networks and state predictors was proposed to achieve satisfactory trajectory tracking performance in the presence of unstructured uncertainty.This method leverages the feature extraction capabilities of deep neural networks to design feature vectors for unstructured uncertain-ties,thereby enhancing the uncertainty estimation capability of the control system.The adaptive law was derived based on the non-smooth Lyapunov stability theory to ensure the stability of deep neural network applications in the control system.Uncertainty was compensated according to the estimated value obtained,resulting in improved trajectory tracking and attitude control performance.Finally,numerical simulations demonstrate that the proposed method improves the trajectory tracking accuracy of unmanned aerial vehicles under the influence of unstructured uncertainties,ensuring the stability and safety of unmanned aerial vehicle swarm flight.关键词
模型参考自适应控制/深度神经网络/状态预测器/非结构化不确定性Key words
model reference adaptive control/deep neural network/state predictor/unstructured uncertainty分类
航空航天引用本文复制引用
程喆坤,赵良玉..基于深度神经网络与状态预测器的无人飞行器自适应控制[J].固体火箭技术,2025,48(5):799-806,8.基金项目
国家自然科学基金项目(12072027) （12072027）
河南省重点研发专项项目(241111222000) （241111222000）
河南省通用航空技术重点实验室开放基金项目(ZH-KF-230201) （ZH-KF-230201）
旋翼空气动力学重点实验室研究开放课题(RAL20200101) （RAL20200101）
第二十七届中国科协年会学术论文. （）
