函数型数据分析与优化极限学习机结合的弹药传输机械臂参数辨识  

Parameter identification of a shell transfer arm using FDA and optimized ELM

作　　者：赵抢抢[1] 侯保林[1] 

机构地区：[1]南京理工大学机械工程学院,南京210094

出　　处：《工程科学学报》2017年第4期611-618,共8页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(51175266);国家高技术研究发展计划资助项目(6132490102)

摘　　要：为实现弹药传输机械臂中不可测参数的辨识,建立了机械臂的虚拟样机,并将其作为样本数据的来源;考虑到样本数据的连续性和平滑特性,使用函数型数据分析和函数型主成分分析对样本数据进行了特征提取,并利用提取的特征参数和待辨识参数作为训练样本对极限学习机(ELM)进行了训练.为提高极限学习机的辨识精度和泛化能力,利用粒子群算法对极限学习机的输入层与隐含层的连接权值和隐含层节点的阈值进行了优化.最后,分别利用仿真数据与测试数据对此方法进行了验证,仿真数据的辨识结果表明,优化后的极限学习机具有更高的辨识精度和泛化能力;同时,通过对比将测试数据的辨识结果代入模型中进行仿真得到的支臂角速度与测试角速度,验证了此方法的可行性和有效性.To identify the unmeasurable parameters of a shell transfer arm,a virtual prototype of the shell transfer arm was built,and the built virtual prototype is regard as the source of the sample data. Considering the continuity and smoothness properties of the sample data,features of the curves were extracted by functional data analysis and functional principal component analysis,and the features and unknown parameters were used to train the extreme learning machine（ ELM）. At the meantime,the weight connecting the input layer and hidden layer and the threshold of the hidden nodes were optimized by particle swarm optimization（ PSO） to improve the identification accuracy and generalization performance of ELM. At last,the presented method was verified by simulation data and test data. The identification results of the simulation data show that the optimized ELM has higher identification accuracy and better generalization performance. Also,the presented method is proved to be feasible and effective by comparing the real angular velocity and the angular velocity from the virtual prototype with respect to the test data identification results.

关 键 词：参数辨识 函数型数据分析 极限学习机 粒子群优化 弹药传输机械臂 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置] TH113[自动化与计算机技术—控制科学与工程]