基于神经网络的无人船自主靠泊模拟研究

    A Neural Network-based Unmanned Ship Autonomously Berthing Controller

    • 摘要: 针对传统神经网络自主靠泊控制器忽视车令与螺旋桨转速、舵令与实际舵角之间的映射关系问题,将控制器的输出由螺旋桨转速、舵角改为车令与舵令。为解决靠泊完成后船艏向偏差较大问题,将多初始状态下的靠泊训练数据改为单一初始状态,并将选择靠泊过程中的部分数据作为控制器输入改为提取靠泊过程中的全部信息,以此来提高控制器的训练效果。仿真实验验证了在不影响其泛化性能的前提下,控制器能成功减小靠泊完成后的船艏向偏差,并且完成船舶靠泊任务。

       

      Abstract: The output of the neural network berthing controller is the form of engine/helm order instead of propeller revolution speed/rudder angle control commands as traditional controllers may give. Doing this is to emphasize the mapping between engine order/helm order and propeller speed/rudder angle which is ignored by traditional controllers. The training of the network is changed from multiple initial states into single initial state. In addition, the training inputs are extended to cover all information extracted from birthing processes while they are tailored in traditional controllers. Simulation is performed to verify the advantages of the controller.

       

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