事件触发混合非零和游戏模块化机器人操纵器的最佳控制,执行协调操作任务
IEEE transactions on neural networks and learning systems
|August 18, 2025
概括
本研究介绍了模块化机器人操纵器的事件触发最佳控制方法,提高了组装和运输等复杂任务的协调. 该方法通过解决动态不确定性,确保稳定高效的人机协作.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 高性能智能机器人的协调控制是一个不断增长的研究领域.
- 人与机器人的交互带来了挑战,因为潜在的冲突和控制不一致.
研究的目的:
- 为模块化机器人操纵器 (MRM) 系统开发事件触发混合非零和游戏的最佳控制方法.
- 解决组装,处理,安装和运输等任务中的协调控制问题.
主要方法:
- 已建立的MRM子系统动态模型使用联合扭矩反和任务分配.
- 开发了一个适应性神经网络 (NN) 标识符,用于未知的动态和摩擦错误.
- 用差分游戏理论将协调问题转化为混合的非零和游戏.
- 大致的最佳值函数和基于事件的哈密尔顿方程,用一个批评者NN.N.来解决.
主要成果:
- 适应性NN标识符确保了重量误差的趋同到最小的0附近.
- 开发的控制策略为每个联合模块和操作对象提供了最佳的控制策略.
- 利亚普诺夫理论证实了在拟议的控制方法下系统的稳定性.
结论:
- 事件触发混合非零和游戏的最佳控制方法有效地解决了MRM系统中的协调挑战.
- 该方法在处理动态不确定性和确保系统稳定性方面表现强.
- 实验验证证了对协调机器人操作的拟议控制策略的实际有效性.
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