<p>The occurrence conditions of underground coal seams are complex and varied, and the working conditions of mining equipment are extremely harsh. Unmanned and intelligent mining has put forward urgent requirements for the adaptability and compliance of excavation equipment. At present, roadheader mainly achieves adaptive control by manually adjusting the cutting head swing velocity. When there is a large-scale sudden change in the coal-rock hardness, the current technology is difficult to ensure the optimal comprehensive cutting performance. To improve the performance under steady-state and abrupt conditions, a multi-objective optimization model for comprehensive cutting performance was firstly established, with the traction swing velocity and cutting rotation speed as the optimization variables, and the coal-rock productivity, cutting area, cutting specific energy consumption, and gear dynamic load as optimization sub-objectives. The particle swarm algorithm was used to obtain the optimal cutting motion parameters for different coal-rock hardness, and the cutting performance indicators for optimal parameter control and traditional control were compared. Then, the coordinated regulation strategy of swing velocity and rotation speed under sudden-change coal-rock hardness condition was studied through the simulation and experiment. The influences of motion parameter regulation sequence, rotational speed adjustment time, and trajectories on the cutting performance were analyzed, respectively. Finally, the comprehensive comparison was conducted, and the results verified the effectiveness of our proposed collaborative swing velocity and rotation speed optimization control scheme, which helps promote the rapid and intelligent excavation.</p>