目的 针对传统液体灌装机灌装定量控制方法存在控制稳定性不高、控制精度较差等问题，引入RBF神经网络对液体灌装机高精度灌装定量控制。方法 构建液体灌装机伺服驱动计量缸传递函数，结合空间扰动性融合方法实现液体灌装机高精度灌装扰动特征解析;采用参数自适应辨识方法进行液体灌装机高精度灌装的定量分析;通过B样条曲线拟合方法对液体灌装机灌装拟合控制;通过自适应参数调节，构建RBF神经网络模型，实现液体灌装机高精度灌装定量控制的优化设计。结果 仿真结果表明，所提方法的液体灌装定量控制稳定性较好，灌装机灌装定量控制效果较好。结论 文中方法提高了液体灌装机高精度灌装的定量控制能力。

Aiming at the problems of traditional filling quantitative control methods of liquid filling machine, such as low control stability and poor control accuracy, this paper introduces RBF neural network to high-precision filling quantitative control of liquid filling machines. The servo drive metering cylinder transfer function of liquid filling machine was constructed, and the high precision filling disturbance characteristic analysis of liquid filling machine was realized by combining the spatial perturbation fusion method. The parameter adaptive identification method is used for the quantitative analysis of the high-precision filling of the liquid filling machine, and the filling of the liquid filling machine is controlled by the B-spline curve fitting method. Through adaptive parameter adjustment, the RBF neural network model is constructed to realize the optimized design of high-precision filling quantitative control of the liquid filling machine. The simulation results show that the stability of liquid filling quantitative control of the proposed method is better, and the filling quantitative control of the filling machine is better. This method improves the ability of high-precision filling quantitative control of the liquid filling machine.