He Boning, Zhu Guoli, Han Lei, Zhang Dailin
State Key Lab of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2020 Dec 24;21(1):61. doi: 10.3390/s21010061.
In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.
在隧道掘进机（TBM）中，实时获取姿态对驾驶员来说非常重要。然而，当前的激光瞄准系统在获取姿态之前存在较大延迟。因此，提出了一种基于自适应神经模糊的信息融合方法来实时预测激光瞄准系统的姿态。在所提出的方法中，采用双速率信息融合来融合激光瞄准系统和双轴倾角仪的信息，然后以更高的速率获得横滚角和俯仰角，并提供更平滑的姿态预测。考虑到存在测量误差，提出了自适应神经模糊推理系统（ANFIS）对测量误差进行建模，然后将基于ANFIS的模型与双速率信息融合相结合以实现高性能。实验结果表明，基于ANFIS的信息融合能够提供更高的姿态预测实时性能和准确性。实验结果还验证了基于ANFIS的信息融合能够解决激光瞄准系统信号丢失的问题。
