适应性神经网络基于强大的控制的空中操纵器的合规接触力控制
1School of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究介绍了空中操纵器的强有力的控制策略,确保复杂的空中操作的精确接触力控制. 该方法通过自适应神经网络和阻抗控制来提高稳定性和准确性.
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
- 人工智能的人工智能
背景情况:
- 空中操纵器将机器人的能力扩展到空中环境中.
- 精确的接触力控制对于空中操纵机任务至关重要.
- 现有的方法可能会与系统不确定性和复杂动态作斗争.
研究的目的:
- 为空中操纵器制定一个强大的控制策略.
- 为了实现准确的轨迹跟踪和符合接触力控制.
- 为了提高空中操纵系统的稳定性和强度.
主要方法:
- 多旋转机和操纵机动力学的数学建模.
- 开发一个强大的滑动模式控制策略.
- 整合一个自适应的辐射基函数神经网络来弥补不确定性.
- 阻抗控制器的设计,用于符合规定的力控制.
主要成果:
- 拟议的控制策略确保了准确的轨迹跟踪.
- 该系统展示了精确控制接触力.
- 适应性神经网络有效地弥补了系统的不确定性.
- 实验结果验证了控制器的稳定性和准确性.
结论:
- 开发的控制方法可以为空中操纵器提供精确而强大的接触力控制.
- 滑动模式控制,自适应神经网络和阻抗控制的集成提供了一个全面的解决方案.
- 这种方法显著提高了空中操纵器的操作能力.
关键词:
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