[PERSON] [13] designed a skidding control system for a deep-sea tracked vehicle over certain types of soft ground to determine the most likely skidding and make use of the driving force. The above-mentioned studies provide references for the path tracking control of tracked vehicles from different viewpoints. However, research specifically referring to the systematic scheduling and tracking control strategy for the roadheader underground is not abundant. A few examples are as follows. [PERSON] [14] integrated vision and inertial navigation to measure the posture of the roadheader and used fuzzy PID and neural network PID to correct the pose deviation. [PERSON] [15] proposed a pose detection system for the roadheader underground using the SINS together with laser sensing, and [PERSON] [16] then proposed an autonomous rectification drive control strategy based  
Figure 1: The structure of a boom roadheader. 1—cutting mechanism; 2—shovel plate mechanism; 3—frame; 4—conveyor; 5—walking mechanism; 6—hydraulic pressure system; 7—control system. on a neural network PID algorithm, according to the skidding rate under different driving environments. [PERSON] [17] also introduced general autonomous sensing and control technologies that involved intelligent rapid heading in coal mining. These works focus more on practical inertial posture detection and navigation for the roadheader as well as the basic control model for this bulky tracked vehicle. We should point out that the inevitable errors in the execution of the pose detection and vehicle adjustment would decrease the effect of path tracking control of the roadheader. To deal with errors, most scholars use filtering to estimate the current pose and predict the next possible pose of the vehicle. [PERSON] [18] introduced Kalman filtering and adaptive Kalman filtering to reduce environmental interference, thereby improving the tracking performance of deep-sea tracked mining vehicles. [PERSON] [19] and [PERSON] [20] also improved the Kalman filter according to the physical characteristics of noise and the sensory characteristics, respectively, to improve the pose detection accuracy. [PERSON] [21] and [PERSON] [22] adopted UKF and EKF, respectively, to achieve high positioning accuracy within UWB pose detection systems. To summarize, there is a need for the improvement of the path correction strategy in terms of dynamic adaptability as well as the ability to deal with possible maneuver and measurement errors in the process of roadheader scheduling and control. This paper proposes a path tracking control based on the pose deviation model for the roadheader, combining BP neural networks and state estimation. It improves the control model using BP neural networks while considering the inevitable pose measurement error and process error using state estimation. Thus, the control law is designed to update in a timely fashion and the control instruction is revised at the same time by implying the SVD-Unscented Kalman filtering. This strategy is believed to make the path tracking control of the roadheader more robust when facing uncertainty as a result of pose detection errors and track skidding, without adding complications due to extra devices or algorithms, which ensures ease of use in industrial coal mining applications.