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采用神经网络的双吊车自适应防摆控制

文天赐 方勇纯 卢彪

文天赐, 方勇纯, 卢彪. 采用神经网络的双吊车自适应防摆控制. 自动化学报, 2023, 49(1): 111−121 doi: 10.16383/j.aas.c211062
引用本文: 文天赐, 方勇纯, 卢彪. 采用神经网络的双吊车自适应防摆控制. 自动化学报, 2023, 49(1): 111−121 doi: 10.16383/j.aas.c211062
Wen Tian-Ci, Fang Yong-Chun, Lu Biao. Adaptive antiswing control for underactuated dual overhead crane system using neural network. Acta Automatica Sinica, 2023, 49(1): 111−121 doi: 10.16383/j.aas.c211062
Citation: Wen Tian-Ci, Fang Yong-Chun, Lu Biao. Adaptive antiswing control for underactuated dual overhead crane system using neural network. Acta Automatica Sinica, 2023, 49(1): 111−121 doi: 10.16383/j.aas.c211062

采用神经网络的双吊车自适应防摆控制

doi: 10.16383/j.aas.c211062
基金项目: 先进计算与关键软件海河实验室基金, 广东省机器人与智能系统重点实验室开放基金资助
详细信息
    作者简介:

    文天赐:南开大学机器人与信息自动化研究所博士研究生. 主要研究方向为欠驱动系统非线性控制. E-mail: wentc@mail.nankai.edu.cn

    方勇纯:南开大学机器人与信息自动化研究所教授. 主要研究方向为非线性控制, 机器人视觉伺服控制, 欠驱动系统控制和基于原子力显微镜的纳米系统. 本文通信作者. E-mail: fangyc@nankai.edu.cn

    卢彪:南开大学机器人与信息自动化研究所讲师. 主要研究方向为欠驱动系统非线性控制. E-mail: lubiao@mail.nankai.edu.cn

Adaptive Antiswing Control for Underactuated Dual Overhead Crane System Using Neural Network

Funds: Supported by Haihe Laboratory Fund of Information Technology Application Innovation and Opening Project of Guangdong Provincial Key Laboratory of Robotics and Intelligent System
More Information
    Author Bio:

    WEN Tian-Ci Ph.D. candidate at the Institute of Robotics and Automatic Information Systems, Nankai University. His main research interest is nonlinear control of underactuated systems

    FANG Yong-Chun Professor at the Institute of Robotics and Automatic Information Systems, Nankai University. His research interest covers nonlinear control, robot visual servoing control, control of underactuated systems and AFM-based nano-systems. Corresponding author of this paper

    LU Biao Lecturer at the Institute of Robotics and Automatic Information System, Nankai University. His main research interest is nonlinear control of underactuated systems

  • 摘要: 由于工业实践对运输能力提出了更高的要求, 双吊车的应用日益广泛. 然而其动力学模型非线性很强, 因此控制器结构十分复杂. 另一方面, 大型货物的摆动很难抑制, 这给双吊车的自动化带来了巨大的挑战. 为了处理以上问题, 首先, 采用神经网络准确地估计了系统的模型, 在此基础上提出了一种自适应防摆控制方法, 很好地实现了双吊车系统的防摆控制; 然后, 采用李雅普诺夫方法, 严格地证明了系统在平衡点的渐近稳定性; 最后, 通过大量的实验结果, 验证了该方法具有良好的性能.
  • 图  1  双吊车系统示意图

    Fig.  1  Illustration of dual overhead crane system

    图  2  实验平台

    Fig.  2  Experimental testbed

    图  3  实验1: 系统状态变量

    Fig.  3  Experiment 1: System state variables

    图  4  实验1: 系统控制输入$F_1$$F_2$和神经网络权值$W_1$$W_2$$V_1$$V_2$的范数

    Fig.  4  Experiment 1: The control input $F_1,\ F_2$ of system and the norm of the NN weights $W_1,\ W_2,\ V_1,\ V_2$

    图  5  实验2: 参数不确定性实验

    Fig.  5  Experiment 2: Test of parameters uncertainty

    图  6  实验2: 初始摆角扰动实验

    Fig.  6  Experiment 2: Test of initial swing disturbances

    图  7  实验2: 不同运输任务实验

    Fig.  7  Experiment 2: Test of different transportation tasks

    表  1  实验1的性能指标

    Table  1  Performance indices of Experiment 1

    性能指标 本文方法 PD LQR PID SMC
    $e_{1xs}\,({\rm{ m}})$ ${\bf{-0.003}}$ −0.028 −0.032 −0.007 0.001
    $e_{2xs}\,({\rm{ m}})$ −0.003 −0.004 −0.004 −0.004 ${\bf{-0.001}}$
    $\theta_{1{\rm{max}}}\,(^\circ)$ ${\bf{0.582}}$ 7.149 3.666 7.746 1.357
    $\theta_{2{\rm{max}}}\,(^\circ)$ ${\bf{0.636}}$ 5.908 4.387 5.401 2.481
    $\theta_{3{\rm{max}}}\,(^\circ)$ ${\bf{0.002}}$ 0.472 0.187 0.452 0.056
    $\theta_{1res}\,(^\circ)$ ${\bf{0.293}}$ 4.160 3.350 1.515 1.274
    $\theta_{2res}\,(^\circ)$ ${\bf{0.000}}$ 2.926 3.053 1.273 1.145
    $\theta_{3res}\,(^\circ)$ ${\bf{0.000}}$ 0.085 0.068 0.016 0.001
    $\Delta_{x{\rm{max}}}\,({\rm{ m} })$ ${\bf{0.905}}$ 0.924 0.994 0.920 0.938
    $\Delta_{x{\rm{min} } }\,({\rm{ m} })$ ${\bf{0.899}}$ 0.749 0.848 0.697 0.894
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出版历程
  • 收稿日期:  2021-11-10
  • 录用日期:  2022-04-12
  • 网络出版日期:  2022-12-21
  • 刊出日期:  2023-01-07

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