基于支持向量回归机学习的机械臂视觉反馈模糊控制  

Visual Feedback Fuzzy Control for a Robot Manipulator Based on SVR Learning

作　　者：张宪霞[1] 章进强 李致远 马世伟[1] 杨帮华[1] Zhang Xianxia;Zhang Jinqiang;Li Zhiyuan;Ma Shiwei;Yang Banghua(School of Mechatronics and Automation,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学机电工程与自动化学院,上海200444

出　　处：《系统仿真学报》2020年第10期1997-2009,共13页Journal of System Simulation

基　　金：National Defense Basic Scientific Research Program of China(JCKY2017413C002)。

摘　　要：针对于机器人无标定视觉伺服问题,提出一种基于支持向量回归机(Support Vector Regression,SVR)学习的模糊控制(Fuzzy Logic Control,FLC)方法。FLC直接用于构建图像特征与机器人关节运动之间的非线性映射关系。FLC的模糊基函数用作SVR的核函数,建立FLC与SVR的数学等价关系。SVR从数据中学习的支持向量构建FLC的规则。所有规则来自于数据,因此无需人工设计规则。本文所提出方法充分利用了SVR针对小数据量学习具有较好的泛化性能优势,实验结果表明该视觉伺服控制器在精度上及收敛上均具取得较好性能。A fuzzy controller based on SVR learning is proposed for uncalibrated robot visual servoing.In this paper,a fuzzy controller is used to directly construct the nonlinear mapping between image features and robot joint motion.The fuzzy basis function of the fuzzy controller is taken as the kernel function of an SVR and the equivalent relationship between the SVR and the fuzzy controller is established.The learned support vector from the SVR is used as the rule of the fuzzy controller.Since all rules are learned from the data,there is no need to manually design the rules.The proposed method fully utilizes the good generalization ability of SVR in small sample learning,and the experimental results show that the proposed visual servoing controller has good performance in precision and convergence.

关 键 词：视觉伺服 机器人 模糊控制 支持向量回归机 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]