Teng Yang

This paper proposes a path correction scheduling strategy for the underground mining boom roadheader by ably combining a back propagation (BP) neural network and state estimation. First, a pose deviation-based tracking model is designed for the roadheader, and it is then further studied and optimized by incorporating the benefits of BP neural networks into the model adaptation. Considering the fact that there is skidding between tracks on the ground and errors during the instant pose detection of the roadheader underground, singular value decomposition (SVD)–Unscented Kalman filtering is applied to estimate the real pose deviation, based on the summarized distribution regularities of the track skidding ratios and the pose detection errors, instead of complicated analysis mechanisms. The BP neural network and states estimation are well combined in structure, enabling this scheduling strategy to update the control law and revise the control instruction simultaneously in the procedure. The proposed path tracking model for the roadheader is simple and clear, without adding extra devices or massive algorithms, which is attractive in terms of industrial use. The path tracking simulations show that this proposed strategy achieves path tracking well in different scenarios and is of high adaptability when facing complex trajectory while still giving stable control instructions for the roadheader.