一个基于神经网络的新型非线性控制器,用于船上旋转起重机,以对抗随机波干扰
Shujie Wu1, Yuzhe Qian1, Haibo Zhang1
1School of Artificial Intelligence, Hebei University of Technology, Xiping Road No. 5340, Beichen District, Tianjin, 300401, China.
本研究介绍了用于3D船舶旋转起重机的神经网络自适应控制方法,有效地管理海洋干扰和未知的参数,以确保稳定的载荷运输.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 海洋工程 海洋工程
- 机械工程 机械工程
背景情况:
- 船上的旋转起重机是复杂的,不太精确的系统.
- 它们容易受到非线性动力学,海洋波/电流干扰以及未知的参数的影响.
- 精确的载荷运输在海上作业中至关重要.
研究的目的:
- 为3D船上旋转起重机提出一个强大的控制方法.
- 为应对非线性,外部干扰和参数不确定性所带来的挑战.
- 为了提高载荷处理操作的精度和稳定性.
主要方法:
- 开发一个神经网络自适应控制方案.
- 实施适应性法,以补偿未知的系统参数.
- 使用利亚普诺夫定理和拉萨尔不变原理进行数学稳定性分析.
主要成果:
- 拟议的控制方法有效地弥补了复杂的干扰.
- 适应性定律成功地减轻了未知的参数的影响.
- 模拟显示负载摆动角度峰值低于2.6度.
- 悬臂的定位精度误差保持在0.16度之内.
结论:
- 神经网络自适应控制方法对3D船上旋转起重机有效.
- 该方法确保在具有挑战性的海洋条件下稳定而准确的载荷运输.
- 控制策略提高了海上起重机操作的可靠性.
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