基于模型参考自适应控制的挖掘机动臂控制特性研究
Study on Control Characteristics of Excavator Boom Based on Model Reference Adaptive Control
陈茂泽 1刘凯磊 2强红宾 3康绍鹏 4钟海防 4张钰琦4
作者信息
- 1. 江苏理工学院机械工程学院,江苏常州 213001;中国重型汽车集团有限公司,山东济南 250102
- 2. 江苏理工学院机械工程学院,江苏常州 213001;江苏大学流体机械工程技术研究中心,江苏镇江 212013;国机重工集团常林有限公司,江苏常州 213136
- 3. 江苏理工学院机械工程学院,江苏常州 213001;江苏大学流体机械工程技术研究中心,江苏镇江 212013
- 4. 江苏理工学院机械工程学院,江苏常州 213001
- 折叠
摘要
针对非线性因素导致的挖掘机动臂自动挖掘时位置控制不稳定的问题,提出一种添加扩展状态观测器的模型参考自适应抑制干扰的方法.建立动臂的电液控制数学模型,搭建仿真模型,采集优秀的控制曲线进行参数辨识,得到具体的传递函数,并以此传递函数作为参考模型.针对不确定的干扰引入扩展状态观测器,并将被控对象的不确定性和外部扰动等同于总扰动.使用遗传粒子群优化算法对控制参数进行优化,最后进行仿真和实验验证.仿真和实验结果表明:相比普通的模型参考自适应控制,使用GAPSO和ESO的模型参考自适应控制的精度、抗干扰能力以及稳定性有很大提升.
Abstract
In addressing the issue of unstable position control of excavator booms caused by nonlinear factors,a model reference adaptive disturbance suppression method was proposed by incorporating an extended state observer(ESO).A mathematical model for the electro-hydraulic control of the boom was established,and a simulation model was constructed.Excellent control curves were collect-ed for parameter identification to determine the specific transfer function,which was used as the reference model.To address uncertain disturbances,an ESO was introduced,and the uncertainties and external disturbances of the controlled object were treated as total dis-turbances.The genetic algorithm particle swarm optimization(GAPSO)was employed for control parameter optimization.Finally,simula-tion and experimental verification were conducted.The results demonstrate that the model reference adaptive control using GAPSO and ESO significantly improves precision,anti-interference capability and stability compared to traditional model reference adaptive control.
关键词
模型参考自适应/扩展状态观测器/参数辨识/动臂/遗传粒子群优化Key words
model reference adaptive control/extended state observer/parameter identification/excavator boom/genetic algorithm particle swarm optimization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据