基于后退的智能控制拖拉机和拖车的移动操纵器,考虑轮滑
Soni1, Naveen Kumar2
1Department of Mathematics, National Institute of Technology Kurukshetra, Kurukshetra 136119, Haryana, India.
ISA transactions
|July 30, 2024
概括
使用神经网络的新型混合控制策略解决了拖拉机拖车移动操纵器的挑战,例如轮子滑动和干扰. 这种适应性方法确保了稳定的控制和快速跟踪错误的融合,以提高性能.
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:
- 拖拉机拖车移动操纵器面临的挑战是未知的轮滑和系统不确定性.
- 基于传统模型的后退控制与这些复杂的动态作斗争.
- 外部干扰进一步降低了系统性能和控制精度.
研究的目的:
- 为拖拉机和拖车的移动操纵器提出混合后退控制策略.
- 为了减轻未知的轮子滑动和外部干扰的影响.
- 为了提高控制系统的稳定性和稳定性.
主要方法:
- 一种混合控制策略,将神经网络与后退控制相结合.
- 一个带有适应术语的动力控制模型来管理轮子滑动.
- 辐射基础功能神经网络以近似未知的系统动态.
- 控制法中的适应性术语,以处理网络重建错误和干扰.
主要成果:
- 建议的控制策略有效地将轮子滑动和干扰的影响降到最低.
- 神经网络准确地接近系统的非线性动态.
- 使用利亚普诺夫定理和巴巴拉特定理,保证了控制方法的稳定性.
- 模拟结果表明有边界的跟踪错误迅速趋于零.
结论:
- 基于混合神经网络的后退控制器为拖拉机和拖车的移动操纵器提供了强大的解决方案.
- 控制器的适应性增强了它处理不确定性和干扰的能力.
- 该方法提供稳定和准确的轨迹跟踪,通过模拟验证.
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