摘要
针对四旋翼无人机高精度轨迹跟踪问题,充分考虑外部扰动等因素,提出了一种带遗忘因子的基于可变增益的前馈-反馈鲁棒迭代学习控制算法。其中,前馈迭代学习控制器采用选择迭代学习方案,弥补了传统的基于遗忘因子迭代学习控制方法的不足,且控制器学习增益可随迭代变化,可以加快算法的收敛性。反馈控制器采用比例-微分控制器,可以保持系统稳定并且加快跟踪误差收敛速度。最后通过收敛性分析和四旋翼仿真试验验证了所提出算法的有效性,所设计算法具有很好的鲁棒性和较高精度。
Considering the external disturbance, a feedforward-feedback robust iterative learning control(ILC) algorithm with forgetting factor based on variable learning gain was proposed to address the quadrotor trajectory’s high precision tracking problem. In the control scheme, the feedforward ILC adopted a selective iterative learning algorithm, which overcomes the shortcoming of the traditional ILC method with forgetting factor, and the learning gain can be changed with iterations,which can increase convergence speed of the algorithm. The feedback controller adopted the PD control method, which can maintain the stability of the system and speed up the convergence of tracking error. Finally, the effectiveness of the proposed method was verified by the convergence analysis and the simulation experiment. The proposed algorithm has strong robustness and high control precision.
作者
陈铁义
朱承治
魏文力
刘周斌
柴盛
于淼
缪宁杰
CHEN Tieyi;ZHU Chengzhi;WEI Wenli;LIU Zhoubin;CHAI Sheng;YU Miao;MIAO Ningjie(Shuangchuang Center,State Grid Zhejiang Electric Power Co.Ltd.,Hangzhou 310052,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310058,China)
出处
《飞行力学》
CSCD
北大核心
2022年第2期46-52,60,共8页
Flight Dynamics
基金
国家电网有限公司总部管理双创孵化培育基金资助项目。
关键词
迭代学习控制
遗忘因子
四旋翼无人机
轨迹跟踪
iterative learning control
forgetting factor
quadrotor UAV
trajectory tracking
