Multi-Sensor Fusion and Error Compensation of Attitude Measurement System for Shaft Boring Machine

To ensure that the shaft boring machine (SBM) runs along the pre-designed axis steadily, the role of the attitude measurement system is essential, but its accuracy and reliability cannot be guaranteed. Currently, there is no effective technology to meet the actual requirements, and there is a lack of relevant theoretical research in this field. Through further study of the attitude analysis method and multi-sensor fusion technology, this paper presents a dual coordinate method, which can be used to describe the attitude characteristics of the SBM. Moreover, this paper discusses the relationships between the attitude changes and the values of the angle as well as displacement and analyzes the implementation complexity and computational efficiency of related algorithms in software and hardware. According to the working characteristics of the SBM, the hardware design and the reasonable layout of the attitude measurement system are provided. Based on multi-sensor data, this paper puts forward an improved method combining a complementary filter with an extended Kalman filter (EKF) for attitude estimation and error compensation. The simulation experiments of different working processes verify the steady-state response and dynamic response performance of the method. Experimental results show that the dual coordinate method and the proposed filter are more suitable for attitude estimation of the SBM compared to other methods.