基于神经元的瓦楞横切机实时控制系统设计
发布时间：2018-08-15 16:54
【摘要】：对基于瓦楞横切机的交流伺服控制系统进行数学建模并进行理论分析。为解决瓦楞机上位机操作系统实时性能差的缺点并提高控制系统的控制精度,系统采用"Windows+RTX"作为瓦楞机的上位机软件运行平台。DSP作为目标嵌入式,利用双口RAM实现数据交互,控制系统采用神经元PID的控制算法,使控制系统具有良好的自学习能力、响应速度快、超调量小等优点。试验结果表明,采用此方案,控制系统自动化程度高、切割精度高、性能稳定。
[Abstract]:Mathematical modeling and theoretical analysis of AC servo control system based on corrugated crosscutting machine are carried out. In order to solve the shortcoming of poor real-time performance of the upper computer operating system of corrugated machine and improve the control precision of the control system, the system adopts "Windows RTX" as the software running platform of the corrugated machine, and uses dual-port RAM to realize the data exchange. The control system adopts the control algorithm of neural PID, which makes the control system have the advantages of good self-learning ability, fast response speed and small overshoot. The experimental results show that the control system has the advantages of high automation, high cutting precision and stable performance.
【作者单位】： 南阳理工学院电子与电气工程学院;北京理工大学爆炸科学与技术国家重点实验室;
【基金】：国家自然科学基金项目:变掺杂NEA GaN光电阴极材料特性与量子效率理论研究(61371058)
【分类号】：TP273;TS736
，
本文编号：2184833
[Abstract]:Mathematical modeling and theoretical analysis of AC servo control system based on corrugated crosscutting machine are carried out. In order to solve the shortcoming of poor real-time performance of the upper computer operating system of corrugated machine and improve the control precision of the control system, the system adopts "Windows RTX" as the software running platform of the corrugated machine, and uses dual-port RAM to realize the data exchange. The control system adopts the control algorithm of neural PID, which makes the control system have the advantages of good self-learning ability, fast response speed and small overshoot. The experimental results show that the control system has the advantages of high automation, high cutting precision and stable performance.
【作者单位】： 南阳理工学院电子与电气工程学院;北京理工大学爆炸科学与技术国家重点实验室;
【基金】：国家自然科学基金项目:变掺杂NEA GaN光电阴极材料特性与量子效率理论研究(61371058)
【分类号】：TP273;TS736
，
本文编号：2184833
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