代学习控制的最小能量路径下列任务的第二阶段的MIMO系统:一个间接的参考更新框架
IEEE transactions on cybernetics
|April 15, 2025
概括
本研究引入了运动控制的间接参考更新框架,优化了后续路径的准确性,并减少了制造任务中的能源消耗. 代学习控制 (ILC) 算法增强了工业应用的稳定性.
科学领域:
- 机器人和自动化 机器人和自动化
- 控制系统工程 控制系统工程
- 制造业 制造技术 制造技术
背景情况:
- 路径跟踪对于许多制造任务至关重要,但跟踪时间通常是未指定的.
- 现有的方法可能无法最佳地平衡精度,能源效率和稳定性.
研究的目的:
- 开发一个间接的参考更新框架,以提高后续路径的准确性和稳定性.
- 为了制定一个最佳的路径规划问题,结合系统约束.
- 为了最大限度地减少工业任务的控制能量.
主要方法:
- 制定一个最佳路径规划问题与系统约束.
- 应用一个离散的方法来得出一个最佳的运动形状.
- 用间接参考更新框架设计一个代学习控制 (ILC) 算法.
主要成果:
- 开发的框架最大限度地提高了准确性,同时嵌入了实际约束.
- 为了工业任务,推导出了最大限度地减少控制能量的运动形状.
- 代学习控制 (ILC) 算法证明了精度和能量减少后的优越路径.
结论:
- 间接参考更新框架显著改善了制造业绩效的跟踪路径.
- 该方法为工业机器人系统提供了强大且节能的解决方案.
- 门架机器人测试平台验证了拟议方法的实际有效性.
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