基于障碍Lyapunov函数的自适应控制器,用于复合的笛卡尔-三角形机器人设备,用于精确的时间变化的位置跟踪.
Karen Jazmin Mendoza-Bautista1, L Abril Torres-Mendez1, Isaac Chairez2
1Centro de Investigación y Estudios Avanzados, Unidad Saltillo, Coahuila, Mexico.
ISA transactions
|September 14, 2023
概括
本研究介绍了复合机器人适应性事件驱动控制器,确保准确的轨迹跟踪,同时尊重状态约束. 新的屏障控制方法提高了机器人系统的性能和稳定性.
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
- 机械电子学是什么意思 机械电子学
背景情况:
- 复合机器人设备,结合卡特西安和三角洲机器人,提出复杂的轨迹跟踪挑战.
- 现有的控制方法经常与建模不确定性,外部扰动和严格的状态约束作斗争.
- 确保强大而精确的运动控制对于先进的机器人应用至关重要.
研究的目的:
- 为复合机器人设备设计和评估适应性事件驱动控制器.
- 在建模不确定性和外部干扰下解决轨迹跟踪问题.
- 为了保证满足状态约束,使用屏障Lyapunov函数和适应性收益.
主要方法:
- 开发一个适应性事件驱动的控制策略,用于一个复合的卡特西安-德尔塔机器人系统.
- 使用障碍Lyapunov函数来执行状态约束,并确保跟踪错误的最终边界性.
- 实施时间变化的适应性增益来管理不确定性和干扰.
- 采用基于机器人在预定义区域内移动的事件驱动方法.
主要成果:
- 与传统的线性状态反控制器相比,拟议的自适应屏障控制显示出优越的轨迹跟踪性能.
- 在整个操作过程中,控制器成功满足了预定义的状态约束.
- 平均平方误差的分析证实了自适应屏障控制策略的有效性和好处.
结论:
- 适应性事件驱动控制器为复合机器人系统的轨迹跟踪提供了强大的解决方案.
- 屏障莱普诺夫函数在动态扰动下有效地管理状态约束.
- 开发的控制方法提高了机器人系统的精度和稳定性,优于传统方法.
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