摘要
针对无人机前轮转向操纵系统中机电作动器的负载模拟需要,设计了一种用于复杂交变载荷模拟的电动加载系统。为了降低电动加载系统的控制时滞和多余力矩对系统精度的影响,提出了一种PID控制与迭代学习控制相结合的加载力矩复合控制策略。介绍了电动加载系统的主要组件并给出其数学模型,分析了电动加载系统多余力矩产生的原因,提出了系统控制延时时间的测量方法,设计了基于迭代学习控制与传统PID控制的复合控制器,分析了迭代学习控制器的收敛条件,分别通过多余力矩抑制和动态力矩加载实验验证了控制策略的有效性。与传统的反馈控制加前馈补偿方法相比,所提方法能够消除控制时滞和多余力矩对加载系统的影响,保证电动加载系统的力矩加载精度。
An electric dynamic loading system is designed for the load simulation of electro-mechanical actuator used in UAV front wheel steering control system,which can simulate the complex alternating load.In order to overcome the problem of control time delay and extraneous torque in electric dynamic loading system,a composite control strategy based on traditional PID control and iterative learning control is proposed.On the basis of introducing the composition of the electric dynamic loading system and establishing the mathematical model of the frequency domain of the loading system,this paper analyzes the generation mechanism of the excess torque in the system,puts forward the measurement method of the control delay time of the system,and designs the composite controller which combines the iterative learning control with the traditional PID.The convergence conditions of the iterative learning controller are given by theoretical analysis,and the effectiveness of the control strategy is verified by extraneous torque suppression and dynamic torque loading experiments respectively.Compared with the traditional feedback and feedforward compensation control strategy,this method can eliminate the influence of control time delay and extraneous torque on the loading system,and ensure the torque loading accuracy of the electric dynamic loading system.
作者
代明光
齐蓉
DAI Mingguang;QI Rong(School of Automation,Northwestern Polytechnical University,Xi’an 710072,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2020年第2期340-349,共10页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(51777170)。
关键词
机电作动器
电动加载系统
多余力矩
控制时滞
迭代学习控制
electro-mechanical actuator
electric dynamic loading system
extraneous torque
control time delay
iterative learning control
