桥式起重机神经网络自适应滑模定位防摆控制
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TP 29

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北京建筑大学硕士研究生创新资助项目(PG2023140);住房和城乡建设部研究开发项目(2020-K-150)


Neural Network Adaptive Sliding Mode Positioning and Anti-swing Control of Overhead Crane
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    摘要:

    针对桥式起重机非线性、存在外界干扰的特点,提出了一种神经网络自适应滑模控制器。首先采用拉格朗日法建立桥式起重机动力学模型;然后在分层滑模控制器的基础上,设计了径向基函数(RBF)神经网络权值自适应更新率,利用RBF神经网络补偿系统的非线性与外界干扰引起的不确定上界,并利用粒子群算法对控制器参数寻优,通过构造Lyapunov函数证明了系统的稳定性;最后设计了1组仿真实验和1组在搭建的实验平台上的验证实验,仿真结果表明:在非线性及外界干扰作用下,神经网络自适应滑模控制器可以快速实现小车定位和负载消摆,控制器可以消除不确定上界对系统的影响。实验结果也表明,所设计的控制器可以使桥式起重机达到控制目标,具有一定的抗干扰能力。

    Abstract:

    A neural network adaptive sliding mode controller is proposed for overhead crane with nonlinear and externally disturbance. Firstly, the dynamic model of overhead crane is established by Lagrange method; Then, based on the hierarchical sliding mode controller, the adaptive update rate of the radial basis function (RBF) neural network weights is designed. The RBF neural network is used to compensate the uncertain upper bound caused by the nonlinear and external disturbance in the system, and the particle swarm optimization algorithm is used to optimize the controller parameters. The stability of the system is proved by constructing a Lyapunov function; Finally, one set of simulation experiments and one set of validation experiments on a established experimental platform were designed. The simulation results showed that under the influence of nonlinear and externally disturbance, the neural network adaptive sliding mode controller can quickly achieve the positioning of the trolley and the load swing suppression, the controller can eliminate the influence of uncertain upper bound on the system. The experimental results also show that the designed controller can make the overhead crane reach the control target, and has a certain ability to resist disturbance.

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郭建明,周惠兴,徐佳琦,等. 桥式起重机神经网络自适应滑模定位防摆控制[J]. 科学技术与工程, 2024, 24(18): 7646-7652.
Guo Jianming, Zhou Huixing, Xu Jiaqi, et al. Neural Network Adaptive Sliding Mode Positioning and Anti-swing Control of Overhead Crane[J]. Science Technology and Engineering,2024,24(18):7646-7652.

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  • 收稿日期:2023-07-16
  • 最后修改日期:2024-04-19
  • 录用日期:2023-10-26
  • 在线发布日期: 2024-07-05
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