粉状饲料动态定量称量包装控制系统设计与实现

目的 为提高粉状饲料包装过程的称量精度,提高包装效率,基于RBF神经网络设计一种粉状饲料定量称量包装控制系统.方法 首先介绍系统总体结构,以动态定量称量为主要研究对象,重点分析包装机的给料过程.针对精细给料过程,建立被控对象数学模型,结合传统PID控制和RBF神经网络设计称量控制器.通过RBF神经网络实现PID控制器参数的在线调整,从而提高称量精度.最后进行实际称量试验.结果 试验结果表明,静态和动态称量偏差均可以控制在0.5％以内,计量为25～50 kg,生产能力可以达到900包/h.结论 所述控制系统称量精度较高,具有比较理想的可靠性和稳定性,能够满足包装需求.

Design and Realization of Dynamic Quantitative Weighing and Packaging Control System for Powdered Feed

In order to improve the weighing precision and efficiency of powdered feed packaging, a quantitative weighing and packaging control system is designed based on RBF neural network. Firstly, the overall structure of the system is introduced with dynamic quantitative weighing as the main research object. Aiming at the fine feeding process, the mathematical model of the controlled object is established, and the weighing controller is designed by combining the traditional PID control and RBF neural network. Through RBF neural network, PID controller parameters can be adjusted online to improve the weighing precision. Finally, the actual weighing test is carried out. The test results show that the deviation of static and dynamic weighing can be controlled within 0.5%. The measurement range is 25-50 kg and production capacity can reach 900 packages/hour. The control system has higher weighing accuracy, better reliability and stability, and can meet the packaging requirements.