贝叶斯物理信息神经网络与MIQPSO-倒退控制非均码头起重机中抑制振动
IEEE transactions on cybernetics
|March 11, 2026
概括
这项研究引入了贝叶斯物理信息神经网络 (BPINN),用于精确控制码头起重机,有效抑制电缆振动并提高运输效率. 这种新的方法提高了复杂的海上物流中的跟踪精度和运营性能.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 在工程领域的人工智能.
- 机械工程 机械工程
背景情况:
- 码头起重机面临的挑战包括灵活的电缆振动,有效载荷摇摆和旋转,影响跟踪精度和运输效率.
- 这些系统的建模涉及复杂,时间变化和空间分布的部分微分方程.
- 低调的动态对实现精确的轨迹跟踪构成了重要的控制设计障碍.
研究的目的:
- 为非均码头起重机制定先进的轨迹跟踪策略.
- 为了抑制柔性电缆的振动,减少有效载荷的运动 (摇摆和旋转).
- 提高总体跟踪精度和海运运输效率.
主要方法:
- 提出了一个贝叶斯物理信息神经网络 (BPINN) 框架,将张力约束集成到损失函数中.
- 哈密尔顿蒙特卡洛 (HMC) 采样用于在贝叶斯框架内推断系统状态.
- 差异平面性和自适应后退控制器被用于管理低调动力学,加上多策略改进的量子行为粒子群集优化 (MIQPSO) 进行参数调整.
主要成果:
- 通过结合张力约束,BPINN有效地抑制了柔性电缆的振动.
- 适应式后退控制器,利用差异平面性,确保了系统状态的全球统一的最终边界性.
- MIQPSO方案优化了控制参数,平衡勘探和趋同,以实现强大的性能.
- 模拟和实验验证了该策略能够实现快速,准确的跟踪和显著的振动减少的能力.
结论:
- 拟议的基于BPINN的轨迹跟踪策略为控制码头起重机提供了一个强大的解决方案.
- 基于物理学的神经网络和先进的控制技术的整合显著提高了运营效率和稳定性.
- 该方法在抑制振动和提高追踪精度方面表现出有效性,即使在外部干扰下.
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