Adaptive Optimal Controls for Multi-Driving Gear of Long-Wall Shearer

The conventional coal miner cut-off section uses a single-motor system, which will stall when encountering a hard header surface due to power limitations. In this paper, we improve the multi-motor servo system of coal miner cut-off section and propose an adaptive optimal controller for multi-driven coal miner servo system. The gear power can be changed to be lager with the multi-driver servo motor, which can cut the hard header coal surface effectively. Firstly, the coal miner multi-driven servo system is approximated a neural network. Then, define the optimal performance index with the coal miner servo system states and torques. The adaptive torques are found using an approximation dynamic programming (ADP) technique based on a given performance function that can find the saddle point and minimize the coal miner performance index. The NN weights' convergence is investigated. Finally, a simulation is included to demonstrate the efficacy of the strategies for optimizing coal miner servo system performance.