事件触发的自适应神经阻抗控制机器人系统的机器人系统
概括
这项研究引入了针对机器人的事件触发自适应神经阻抗控制 (ETANIC),降低了计算负载和通信成本. 新方法提高了机器人系统在环境交互任务中的效率.
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
- 人工智能的人工智能
背景情况:
- 机器人系统需要强大的控制,以符合环境的互动.
- 传统的阻抗控制可以是计算密集型和通信繁重的.
- 适应神经阻抗控制 (ANIC) 提供了更好的性能,但仍然需要资源.
研究的目的:
- 为机器人系统开发一个高效的事件触发自适应神经阻抗控制 (ETANIC) 方案.
- 为了降低计算负担和通信成本,同时保持稳定性和性能.
- 提高机器人系统在环境交互任务中的效率.
主要方法:
- 实现了一个事件触发机制与自适应的神经阻抗控制相结合.
- 利用辐射基函数神经网络 (RBFNN) 来估计系统的不确定性.
- 从Lyapunov函数中推导出RBFNN更新规律,并使用Lyapunov理论分析闭环稳定性.
- 设计事件触发条件以防止Zeno行为.
主要成果:
- 拟议的ETANIC方案大大降低了计算和通信需求.
- 埃塔尼克确保机器人系统的稳定性和跟踪性能.
- 数字模拟和实验测试验证了ETANIC对ANIC的优越效率.
- 该系统展示了有效的合规行为和交互能力.
结论:
- 埃塔尼克方案为机器人阻抗控制提供了一种高效和稳定的方法.
- 事件触发机制有效地减少了自适应神经控制中的资源需求.
- 埃塔尼克是复杂环境中机器人交互任务的有希望的控制策略.
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