基于多信息融合的ROV悬停控制研究
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1.东北石油大学电子科学学院 大庆163318;2.东北石油大学机械科学与工程学院 大庆 163318

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TP242.2

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A research on hovering control of ROV by information fusion
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1.School of Physics and Electronic Engineering,Northeast Petroleum University, Daqing 163318,China; 2.School of mechanical science and Engineering of Northeast Petroleum University Northeast Petroleum University, Daqing 163318,China;

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    摘要:

    水下机器人(Remote Operated Vehicle,以下简称ROV)的水下悬停是一种非常重要的运动形式,为了实现水下悬停控制,设计了一种基于多传感器信息融合的悬停控制方法。ROV采用加速度计、陀螺仪和深度计分别获取机体的各轴的加速度、姿态角以及深度等信号,采用四元数对陀螺仪数据进行解算,得到机体实时姿态角,通过加权滑动滤波对陀螺仪和加速度计的数据进行预处理,之后采用卡尔曼滤对加速度计和陀螺仪的数据进行融合,得到实时的加速度,最后通过BP神经网络对深度和姿态角进行预测,并把预测的深度和姿态角带入PID控制器作为控制策略。该融合方法可以有效完成悬停控制,并且实验表明,该方法可以实现较高的稳定性和实时性,在悬停控制中具有广阔的前景。

    Abstract:

    Underwater robot (RemoteOperatedVehicle, hereinafter referred to as the ROV) underwater hovering is a very important shipment.In order to achieve the underwater hovering control, we design a hovering control method based on multi-sensor information fusion. The ROV respectively calculated by accelerometer and gyroscope and depth for the body of the shaft, attitude Angle and depth of acceleration signal of the gyroscope data by quaternions, get the body attitude Angle in real time, through the weighted sliding filter of gyroscope and accelerometer data beforehand. After using kalman filter for accelerometer and gyroscope data fusion, get real-time acceleration. Finally, BP neural network to forecast the depth and the attitude Angle, and the prediction of depth and the attitude Angle into the PID controller as the control strategy. The fusion method can effectively complete the hovering control, and the experimental results show that this method can achieve higher stability and real-time performance, has a broad prospect in the hovering control.

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王明吉,梁斌,任福深.基于多信息融合的ROV悬停控制研究[J].电子测量技术,2021,44(6):166-172

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  • 在线发布日期: 2024-10-18
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