【摘要】 针对采煤机调高系统控制精度低、稳定性差、抗干扰能力弱等问题，以采煤机数字液压缸调高控制系统为研究对象，在BP神经网络PID控制算法基础上，利用改进麻雀搜索算法（SSA）对神经网络的初始权值寻优，仿真并验证改进SSA优化采煤机调高BP-PID控制系统的效果。仿真结果表明，改进SSA优化的BP-PID控制方式的系统阶跃响应超调量比传统PID控制方式减少23.2个百分点，稳态时间减少36.0%，提高了系统的自适应能力，为解决采煤机调高系统控制精度低、响应实时性差等问题提供一种新的方法。更多还原

【Abstract】 Aiming at the problems of low control accuracy,poor stability and weak anti-interference ability of the height adjustment system of the shearer,the digital hydraulic cylinder height adjustment control system of the shearer was taken as the research object.Based on the BP neural network PID control algorithm,the improved sparrow search algorithm (SSA) was used to optimize the initial weights of the neural network,and the effect of the improved SSA optimization of the BP-PID control system for the height adjustment of the shearer was simulated and verified.The simulation results show that the step response overshoot of the system of the BP-PID control method with improved SSA optimization is reduced by 23.2 percentage points compared with the traditional PID control method,and the steady-state time is reduced by 36.0%,which improves the adaptive ability of the system,and provides a new method for solving the problems such as low control accuracy and poor response in real time of the height adjustment system of the shearer.更多还原