摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于机器人的新报告。根据NewsRx记者对沈阳的新闻报道，研究表明：“本文提出了一种考虑物理可行性和非线性摩擦模型的三阶物理参数识别方法，通过构造优化问题，可以在物理可行性下获得完整的物理参数。”本研究的资金来源包括辽宁省基础研究计划，摘要:国家自然科学基金项目(NSFC).新闻记者引用了东北大学的一篇研究文章,采用考虑Stribeck效应的非线性摩擦模型来提高辨识精度.第一回路用回归模型辨识物理参数s;第二回路用回归模型辨识物理参数s .采用非线性优化方法对非线性摩擦模型进行辨识，在第三回路中，对得到的摩擦参数进行更新，对辨识结果进行进一步优化，与传统的最小二乘(LS)、加权最小二乘(WLS)等优化方法不同，这些方法只得到基本参数，没有考虑Stribeck效应，本文提出的SC Heme能够得到具有物理约束的物理参数，在基于模型的控制等机器人应用中有很大的应用价值，同时还利用了Stribeck效应来提高辨识精度。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting originating in Shenyang, People's Republic o f China, by NewsRx journalists, research stated, "This paper proposed a three-lo op physical parameter identification method considering physical feasibility and nonlinear friction model. The full physical parameters can be obtained with phy sical feasibility by constructing an optimization problem."Financial supporters for this research include Liaoning Province Basic Research Program, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from Northeastern Universi ty, "And the nonlinear friction model which considered Stribeck effect is employ ed to improve identification accuracy. In the first loop, the physical parameter s are identified with a regression model. In the second loop, the nonlinear fric tion model is identified with a nonlinear optimization method. And in the third loop, the obtained friction parameters are updated and the identification result s are to be further optimized. Different from traditional methods like the least squares (LS), weight least squares (WLS) and other optimization methods which c an only get base parameters and do not consider Stribeck effect, the proposed sc heme can get physical parameters with physical constraints. It is useful in many robotic applications, like model-based control. The Stribeck effect is also emp loyed to improve identification accuracy."
NETL
NSTL