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机构地区:[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122 [2]哈尔滨工业大学机器人技术与系统国家重点实验室,黑龙江哈尔滨150080
出 处:《计量学报》2013年第3期231-236,共6页Acta Metrologica Sinica
基 金:基金项目:国家自然科学基金(61005080);中央高校基本科研业务费专项(JUSRP11A48)
摘 要:为了克服粉粒状物料称重精度易受非线性、随机干扰和留空物料(投料门关闭时尚在半空中的物料)时变等因素影响及称重效率低的问题,提出了多级投料(大投、中投和变流量细投)、RBF神经网络PID细投控制和自适应的留空物料补偿策略结合的方法。设计了计量秤的机械结构和基于S7-200PLC、Siwarex_MS称重模块和伺服电机的控制系统;伺服电机,执行机构和PLC系统(集成秤重模块)构成闭环控制系统实现多级投料,相比于气缸控制的投料结构能大幅提高称重的精度。针对所设计的计量秤分析了称重原理和称重误差产生因素,采用多级投料:大投和中投保证称重的快速性,智能PID控制的细投保证称重精度。实验结果表明:称重25kg化肥,平均精度为±0.114%,最大称重速度为1100次/h。A dynamic and high precision weighing scale is designed for powder/granular agricultural products. The mechanical structure and the control system of the scale is desigened. The main components of the control system include S7-200 PLC, Siwarex_MS weighing model and servo motor. The servo motor, the PLC system (integrated weighing module) and the implementation mechanical structure constitutes a closed loop control system. Comparing to the open-loop control structure which mainly contain cylinder and single casting gate, it can significantly improve the accuracy of the scale. In order to overcome the nonlinear disturbance factors and the varing air materials ( materials in the air with casting gate closed) which effecting on the weighing accuracy, the RBF neural network PID controller is used to the process of fine materials casting, and the adaptive correction method for the air materials was adopted. In the experiment, 25 kg rice is weighed, with an average accuracy of ±0. 114% and the maxmum speed of 1100 times/h.
关 键 词:计量学 称重控制 伺服电子秤 RBF神经网络 PID控制器 自适应
分 类 号:TB932[一般工业技术—计量学]
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