基于迭代学习和模糊模型的机器人故障估计
Robot Fault Estimation Based on Iterative Learning and Fuzzy Models
沐雅琪 1包西平 1张欣 2王红梅3
作者信息
- 1. 徐州工业职业技术学院汽车工程学院,江苏徐州 221140
- 2. 徐州工业职业技术学院信息工程学院,江苏徐州 221140
- 3. 无锡晋拓材料科技有限公司,江苏无锡 214101
- 折叠
摘要
针对柔性机器人执行器故障问题,提出了基于迭代学习和模糊模型的机器人故障估计策略.首先,为了处理机器人系统的非线性,将机器人系统转化成Takagi-Sugeno(T-S)模糊系统.其次,基于T-S模糊系统和机器人重复运动特性,设计模糊观测器和模糊迭代学习故障估计算法,实现同时精准估计机器人状态和执行器故障.再次,提出可解条件,保证模糊迭代学习故障估计算法的鲁棒单调收敛性,使迭代估计算法具有良好的暂态行为.最后,利用一个单连杆柔性关节机器人来验证所提方法的有效性.
Abstract
A robot fault estimation strategy based on iterative learning and fuzzy models is proposed to address the problem of actuator faults in flexible robots.Firstly,in order to handle the nonlinearity of the robot system,the robot system is transformed into a Takagi Sugeno(T-S)fuzzy system.Secondly,based on the T-S fuzzy system and the repetitive motion characteristics of the robot,a fuzzy observer and a fuzzy iterative learning fault estimation algorithm are designed to achieve accurate estimation of the robot state and actuator faults simultaneously.Once again,propose solvable conditions to ensure the robust monotonic convergence of the fuzzy iterative learning fault estimation algorithm,so that the iterative estimation algorithm has good transient behavior.Finally,a single link flexible joint robot is used to verify the effectiveness of the proposed method.
关键词
柔性机器人/执行器故障/T-S模糊系统/迭代学习故障估计Key words
flexible robots/actuator fault/T-S fuzzy system/iterative learning fault estimation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据