Adaptive Fluid Supply Optimization Method for Automatic Shearer Tracking of Hydraulic Supports in Fully Mechanized Mining Faces

Fixed-rate fluid supply cannot effectively match the highly time-varying demand of hydraulic support groups during automatic shearer-following operations, resulting in redundant fluid supply and pipeline pressure fluctuations. To address this problem, this study proposes an adaptive fluid supply optimization method based on demand prediction. Instantaneous fluid demand models for support lowering, raising, advancing, and conveyor pushing are first developed from hydraulic cylinder dynamics and valve-orifice characteristics. To reduce the dimensionality of continuous profile optimization, a limited number of flow control points are optimized, and a continuous supply flow profile is reconstructed through S-curve interpolation. An optimization model is then formulated to improve pressure stability and fluid-supply utilization while satisfying constraints on pump capacity and the rate of flow variation. Experiments conducted on a hydraulic support group test platform show that the predicted demand profiles agree well with the measured flow rates in terms of their overall trends and peak values, indicating good model–experiment agreement under the investigated conditions. The adaptive fluid supply strategy is subsequently evaluated through hydraulic system simulations of sequential and overlapping support operations. The simulation results show that the adaptive strategy increases fluid supply utilization from 34.96% to 59.47% and from 50.56% to 71.45%, while reducing the pressure fluctuation index by 18.97% and 27.34%, respectively. These simulation results support the potential of the proposed strategy to improve supply–demand matching under the specified conditions; closed-loop experimental validation remains necessary.