神经网络自适应反向控制灵活的关节空间操纵器考虑到重力的影响
Shaoqing Li1,2, Lingcong Meng2, Kai Fang2
1School of Mechanical and Electrical Engineering, Zhangjiakou Vocational and Technical College, Zhangjiakou 075000, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究介绍了一种用于灵活关节操纵器的新型神经网络自适应反向控制算法. 该方法有效地减少了振动,并提高了轨迹跟踪控制,尽管存在重力变化和系统不确定性.
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
- 人工智能的人工智能
背景情况:
- 灵活的关节操纵器在轨迹跟踪方面存在挑战,原因是固有的振动和对重力等环境变化的敏感性.
- 系统的不确定性和外部干扰进一步降低了这些系统的控制性能.
研究的目的:
- 为灵活的关节空间操纵器开发一个强大的控制算法,可以适应不同的重力环境.
- 通过解决系统不确定性,减轻弹性振动,提高轨迹跟踪精度.
主要方法:
- 建立了考虑到重力的灵活关节操纵器的动态模型.
- 单一扰动理论被用来将系统分解成快速和缓慢的子系统.
- 一个神经网络自适应反向控制策略,利用辐射基函数 (RBF) 网络,被设计为缓慢的子系统近似不确定性.
主要成果:
- 拟议的控制方法有效地抑制了由关节灵活性引起的高频弹性振动.
- 该算法证明了对系统不确定性和干扰的稳定性,实现了准确的轨迹跟踪.
- 模拟证实了适应逆向控制在不同重力条件下的有效性.
结论:
- 基于单一扰动理论的神经网络适应反向控制为灵活关节操纵器的轨迹跟踪控制提供了有效的解决方案.
- 这种方法提高了系统的稳定性和抑制振动,使其适用于动态和不确定的环境中的应用.
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