动态输出反和神经网络控制一个非全学移动机器人
Manuel Cardona1, Fernando E Serrano1
1Research Department, Universidad Don Bosco, San Salvador 1874, El Salvador.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种用于非全方位移动机器人的新型动态输出反神经网络控制器,使用利亚普诺夫理论确保稳定性和融合. 该控制器可实现精确的轨迹跟踪,用于增强机器人导航.
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:
- 非全方位的移动机器人由于其动力学约束而存在独特的控制挑战.
- 现有的控制策略往往需要完整的状态信息,这限制了实际实施.
研究的目的:
- 为非全方位移动机器人设计和合成动态输出反神经网络控制器.
- 为稳定和轨迹跟踪制定强大的控制策略.
- 通过使用既定的理论框架,确保系统的稳定性和融合.
主要方法:
- 针对非全方位移动机器人的动态模型的推导,考虑到它们的约束.
- 开发两种控制策略:无漂移控制和轨迹跟踪控制.
- 实现一个动态输出反神经网络控制器.
- 在控制定律设计和稳定性分析中应用利亚普诺夫稳定性定理.
主要成果:
- 一个动态输出反神经网络控制器成功设计和合成.
- 制定了两个不同的控制策略,解决稳定和轨迹跟踪.
- 利亚普诺夫理论证实了移动机器人的位置坐标的稳定性和趋同.
- 数字实验验证了拟议的控制策略的有效性.
结论:
- 拟议的动态输出反神经网络控制器有效地管理非全学移动机器人动态.
- 控制器确保稳定性和准确的轨迹跟踪,适用于先进的硬件.
- 该研究通过数值实验验证实了理论结果,证明了实际可行性.
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