摘要
挖掘机液压系统具有强烈的非线性,现有挖掘机控制普遍采用基于模型的控制方法,需要建立精确的挖掘机模型,成本过高且控制效果差。因此,提出了一种基于回声状态网络的液压挖掘机位置在线学习控制方法,建立了在线学习基本模型,该模型包含2个回声状态网络、1个学习目标的逆和1个基于学习目标逆生成的控制输入,在对其进一步优化后,提出了在线学习优化模型。以正弦信号为参考轨迹,对基础模型和优化模型进行了仿真研究,搭建了挖掘机控制试验装置,分别开展了单关节运动、多关节运动和实际挖掘运动实验,结果表明:采用在线学习控制方法后,挖掘机位置控制精度明显提高,其均方根误差降低占比超过50%,证明了所提出控制方法的性能和可行性。
Hydraulic system of excavator has strong nonlinearity.The control method based on model is widely used in the existing excavator control system.It is necessary to build an accurate excavator model,but the cost is too high and the control effect is poor.Therefore,an online learning control method for hydraulic excavator position was proposed based on the echo state network,and the basic online learning model was established.The model included two echo state networks,an inverse of the learning objective,and a control input based on the inverse of the learning objective.After further optimized the model,an online learning optimization model was proposed.Sinusoidal signal was used as a reference trajectory,the simulation study of the basic model and the optimization model was carried out,and the control test device of the excavator was built.Single joint motion,multi joint movement and actual excavation motion experiments were carried out respectively.The results show that by using the online learning control method,the position control accuracy of the excavator is obviously improved,and the root mean square error is reduced by more than 50%,which proves the performance and feasibility of the proposed control method.
作者
黄晶晶
高培
HUANG Jingjing;GAO Pei(Yancheng Polytechnic College,Yancheng Jiangsu 224005,China;Dongfeng Yueda Kia Motors Co.,Ltd.,Wuhan Hubei 224000,China)
出处
《机床与液压》
北大核心
2020年第21期117-121,共5页
Machine Tool & Hydraulics
基金
江苏省高等学校自然科学研究面上项目(17KJB460121)。
关键词
回声状态网络
挖掘机
在线学习
均方根误差
Echo state network(ESN)
Excavator
Online learning
Root mean square error
