啤酒贮液缸液位稳定控制

Liquid Level Stability Control of Beer Storage Tank

啤酒在生产过程中需要对其进行灌装,而贮液缸液位如果控制不稳定会直接影响啤酒的质量和产量。为精确控制啤酒灌装液位,设计一种基于RBF神经网络PID控制方法。建立贮液缸内液位数学模型,为提高传统PID控制方法的自适应能力,在PID控制中引入神经网络算法,利用神经网络的自我学习能力,实现对PID中比例、积分、微分3个参数的自适应调节。仿真结果表明,经过RBF神经网络调节后的PID,其输出响应曲线调节时间明显缩短,超调量最大为1.8%,采用该控制方法可大幅提高液位控制精度。

Beer needs to be filled in the production process, and if the liquid level of the storage tank is not stable, it will directly affect the quality and output of beer. In order to accurately control the beer filling level, a PID control method based on RBF neural network was designed. In order to improve the self-adaptive ability of traditional PID control method, neural network algorithm was introduced into PID control, and the self-learning ability of neural network was used to realize the self-adaptive adjustment of PID parameters such as proportion, integral and differential. The simulation results showed that the adjusting time of the output response curve of PID adjusted by RBF neural network was obviously shortened, and the maximum overshoot was 1.8%. The control precision of liquid level could be greatly improved by using this control method.