同时学习的基于自适应控制的未执行的机器人系统,保证过渡性性能,无论是执行的和未执行的动作
IEEE transactions on neural networks and learning systems
|September 18, 2023
概括
本研究介绍了适应性控制器,用于未完成的机器人系统,确保所有状态的指数趋同. 它通过使用并发学习来解决不确定性和干扰,提高了安全性和效率.
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
- 应用数学 应用数学 应用数学
背景情况:
- 低调的机器人系统在实现精确的控制和短暂的性能方面面临挑战,特别是对于未调节的变量.
- 现有的方法往往只能保证不对称的稳定性或边界性,缺乏对指数趋同的理论和实际保证.
研究的目的:
- 设计一个适应性跟踪控制器,用于未被调节的系统,以确保对被调节和未被调节状态的指数趋同.
- 在复杂的机器人任务中提高控制精度,过渡性能,安全性和效率.
主要方法:
- 建议采用数据驱动的并发学习 (CL) 方法,以补偿未知的动态和干扰,而不需要激发的持久性或线性参数化.
- 整合了干扰判断机制,以减轻外部干扰的影响.
主要成果:
- 控制器实现了所有系统状态的指数趋同,未被调节的状态趋同到可调节的边界.
- 在运动范围和收速度方面表现出令人满意的性能.
- 该方法提供了理论和实践的保证,暂时性能和指数的收速度为未加时状态.
结论:
- 这项工作提出了第一个理论和实践解决方案,以确保在不确定性和干扰的低值系统中确保过渡性性能和指数趋同.
- 开发的控制器有效地实现了被执行的运动的指数追踪和未执行状态的指数收,通过理论分析和实验验证.
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