摘要
针对四旋翼无人机吊挂负载系统传统建模方法(牛顿-欧拉/拉格朗日)建模步骤繁琐、计算效率低以及负载摆动稳定时间过长的问题。首先,提出一种基于Kane法建立四旋翼吊挂系统动力学模型的方法。该方法无需分析牛顿-欧拉法中的理想约束反力,也不必计算拉格朗日法中的动力学函数及其导数。在此基础上,设计一种基于自适应矩估计-神经网络-PID(Adaptive Moment Estimation-Neural Network-PID,Adam-NN-PID)的抗摆控制器,并搭配一种摆角-位移控制策略,以实现负载快速稳定;最后,在仿真环节中,对系统加入多种风扰,以研究抗摆控制器的动态控制效果。仿真结果表明:相较于传统PID和BPNN-PID摆角控制器,基于Adam-NN-PID设计的抗摆控制器,能更快速的使负载稳定,并且负载摆动幅度更小。
To address the problems of cumbersome modeling procedures,low computational efficiency in conventional methods(Newton-Euler/Lagrange)for quadrotor UAV slung-load systems,and excessive load swing stabilization time,this study first proposes a Kane’s method-based approach for establishing the dynamic model of the quadrotor slung-load system.This method eliminates the need to analyze ideal constraint forces required in Newton-Euler formulations and avoids computations of dynamic functions and their derivatives in Lagrange methods.Building upon this foundation,an anti-swing controller integrating Adaptive Moment Estimation-Neural Network-PID(Adam-NN-PID)is designed,combined with a swing angle-displacement control strategy to achieve rapid load stabilization.Finally,the simulation introduces multiple wind disturbances to investigate the dynamic control performance.Results demonstrate that compared to conventional PID and BPNN-PID swing angle controllers,the proposed Adam-NN-PID anti-swing controller achieves faster load stabilization with reduced swing amplitude.
作者
周嘉星
余子成
高登巍
黄桂兰
陈志高
邓钊
ZHOU Jiaxing;YU Zicheng;GAO Dengwei;HUANG Guilan;CHEN Zhigao;DENG Zhao(Xiamen University of Technology,School of Electrical Engineering and Automation,Xiamen 361024,Fujian,China;Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control,Xiamen 361000,Fujian,China;Xi’an Mordern Control Technology Research Institute,Xi’an 710065,Fujian,China;National Key Laboratory of Land and Air Based Information Perception and Control,Xi’an 710065,Fujian,China)
出处
《弹箭与制导学报》
北大核心
2025年第5期887-899,共13页
Journal of Projectiles,Rockets,Missiles and Guidance
基金
福建省自然科学基金资助(2022J05286)
厦门市科技计划资助项目(3502Z20227072)
厦门理工学院高层次人才科研启动资助项目(YKJ22019R、YKJ24018R)
教育部产学合作协同育人项目(231102532155002)
厦门理工学院研究生创新启动基金(YKJCX2024164、YKJCX2024147)。
