基于扩展状态观察者的动态代学习,用于控制六度自由度操纵器的轨迹跟踪控制
Jiahui Xu1, Dazi Li1, Jinhui Zhang2
1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
ISA transactions
|October 15, 2023
概括
使用扩展状态观察者 (ESO),代学习控制 (ILC) 和模型补偿 (MC) 的新控制方案提高了机器人操纵器轨迹跟踪精度和强度,以应对工业自动化任务中的不确定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 自动化自动化自动化自动化自动化
背景情况:
- 工业自动化依赖于精确的操纵器控制.
- 现有的轨迹跟踪方案面临着诸如高合,复杂的计算和不良稳定性等挑战.
- 多个关节的机器人操纵器需要先进的控制,以便在重复任务中获得准确性.
研究的目的:
- 为了解决机器人操纵器轨迹跟踪的低准确性和不良稳定性.
- 为六度自由度 (六DOF) 操纵器提出一个先进的控制策略.
- 克服在重复性工业任务中的不确定性.
主要方法:
- 代学习控制 (ILC) 结合模型补偿 (MC) 和扩展状态观察者 (ESO).
- ESO估计了外部和内部模型的扰动和不确定性.
- ILC可以快速准确地控制重复的任务,而MC则简化了反向操作.
主要成果:
- 拟议的控制方案证明了操纵器轨迹跟踪的增强精度和稳定性.
- 控制方案的收是数学证明使用Lyapunov函数和时间变化的近似理论.
- 模拟和实验结果验证了拟议策略的有效性.
结论:
- 集成的ESO,ILC和MC战略为机器人操纵器的精确轨迹跟踪提供了强大的解决方案.
- 这种方法有效地处理工业自动化中的模型不确定性和外部干扰.
- 经过验证的方案提供了一种可靠的方法来提高六个DOF操纵器的性能.
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