摘要
为满足连续回转电液伺服马达系统在动态不确定性、参数摄动等未知强非线性和不确定强扰动因素下的精度需求和跟踪性能,提出了一种改进自抗扰控制(ADRC)策略。在阀控液压马达原理的基础上,建立马达五阶闭环系统状态空间模型。利用参数摄动和外部扰动对自抗扰控制器三部分进行改进,引入线性与非线性组合的快速跟踪微分器,提出用滑膜控制改进非线性状态误差反馈控制律,确定非线性补偿的扩张状态观测器。利用灰狼算法分别对三个部分进行参数整定。结果表明,在跟踪精度和响应速度均满足双十指标要求下,改进自抗扰控制器能够使系统频率达到12 Hz,为马达的实际应用奠定了基础。
In order to meet the precision requirements and tracking performance of the continuous rotary motor electro-hydraulic servo system under unknown strong non-linear and uncertain strong disturbance factors,such as dynamic uncertainty and parameter perturbation,an improved active disturbance rejection control(ADRC)strategy was proposed.The state space model of the fifth order closed-loop system was established based on the principle of valve-controlled hydraulic motor.Then the three parts of ADRC were improved by parameter perturbation and external disturbance;the fast tracking differentiator was introduced into linear and non-linear combinations;the nonlinear state error feedback was proposed using synovial control;the extended state observer was determined by nonlinear compensation.In addition,the grey wolf algorithm was used to set the parameters of the three parts.The simulation and experimental results show that the improved ADRC can realize the system frequency 12 Hz when the tracking accuracy and response speed meet the requirements of double ten indexes,which lay foundation for the motor application.
作者
王晓晶
冯亚铭
孙宇微
WANG Xiao-jing;FENG Ya-ming;SUN Yu-wei(Key Laboratory of Advanced Manufacturing and Intelligent Technology(Ministry of Education),Harbin University of Science and Technology,Harbin 150080,China)
基金
Project(51975164)supported by the National Natural Science Foundation of China
Project(2019-KYYWF-0205)supported by the Fundamental Research Foundation for Universities of Heilongjiang Province,China。
关键词
连续回转电液伺服马达
自抗扰控制
快速跟踪微分器
非线性状态误差反馈控制律
扩张状态观测器
灰狼算法
continuous rotary electro-hydraulic servo motor
active disturbance rejection control(ADRC)
fast tracking differentiator(TD)
non-linear state error feedback(NLSEF)
extended state observer(ESO)
grey wolf algorithm