## 6 Conclusions  
This paper proposed a path correction scheduling strategy, namely, a path tracking control model for the underground mining boom roadheader by combining the BP neural network and state estimation ably. It refers to the pose deviation model of general vehicle motion as the basis and designs a path tracking control model to drive the pose deviation  
Figure 13: Path tracking combining state estimation and BP neural network: (**a**) the coincidence between expected and tracking paths; (**b**) the position and orientation errors; (**c**) the required angular speeds of the driving wheels. of the roadheader. The target trajectory converges to zero by recommending angular velocities for the left- and right-side driving wheels according to the local pose detection of the roadheader. In order to improve the engineering applicability of this basic model, the control law is simplified by introducing a set of coefficients, \(k_{x}\), \(k_{y}\), \(k_{\theta}\), which are random positive values corresponding to the position and orientation errors \(x_{e},y_{e},\theta_{e}\), respectively. In view of the phenomenon that different allocation of coefficients \(k_{x}\), \(k_{y}\), \(k_{\theta}\) in the control law leads to different tracking effects, the control law is dynamically optimized by updating coefficients \(k_{x}\), \(k_{y}\), \(k_{\theta}\) using the BP neural network, which is beneficial for dealing with complicated situations, such as devious trajectory for tracking or disadvantageous initial pose deviation. Additionally, considering that there is skidding between tracks upon the ground and that there are errors in the instant pose measurements of the walking roadheader in practice, this paper treats the unpredictable effects of track skidding as the process noise and the pose detection errors as the observation noise. Thus, we solve the uncertainty in the control model by applying the statistical method instead of analyzing the complex mechanism of track skidding or pose detection of a working roadheader. With the summarized distribution regularities of the track skidding ratios and the pose detection errors of certain positioning systems, SVD-Unscented Kalman filtering is applied to estimate the real pose deviation to the tracked path and to renew the scheduling control instruction for the roadheader. The novelty of this scheduling strategy is that by combining the BP neural network and state estimation into the pose deviation-based path tracking control model for the roadheader, the involved control law is enabled to update dynamically according to the local tracking performance; simultaneously, the control instructions have a chance to be further revised by considering errors during the procedure of tracking. This is believed to make the path tracking control of the roadheader more robust when facing uncertainty resulting from pose detection errors and track skidding. The path tracking simulations also show that our proposed strategy is able to achieve path tracking well within a few operations and is of high adaptability when facing complex trajectory while still giving stable control instructions for the driving wheels. The structure of the scheduling strategy is simple and clear, without adding extra devices or massive algorithms; this is attractive in terms of industrial applications in coal mining. Our research goal is to fully study the feasibility and performance of this proposed scheduling strategy for path correction in practical applications of roadway forming using roadheaders in coal mining. Conceptualization, Y.Q. ; methodology, Y.Q. ; software, Y.Q. ; validation, T.Y., T.L. and Y.Z. ; formal analysis, T.Y. ; investigation, T.Y. ; resources, Y.Q. and S.F. ; data curation, T.L. ; writing--original draft preparation, T.Y., T.L. and Y.Z. ; writing--review and editing, Y.Q. ; visualization, Y.Q. ; supervision, T.Y. ; project administration, Y.Q. ; funding acquisition, Y.Q. All authors have read and agreed to the published version of the manuscript.