系统仿真学报 ›› 2016, Vol. 28 ›› Issue (11): 2764-2770.doi: 10.16182/j.issn1004731x.joss.201611017

• 仿真应用工程 • 上一篇    下一篇

基于ELM的水泥立磨生料细度ADP控制

林小峰, 孔伟凯   

  1. 广西大学,广西 南宁 530004
  • 收稿日期:2015-04-08 修回日期:2015-10-18 出版日期:2016-11-08 发布日期:2020-08-13
  • 作者简介:林小峰(1955-), 男, 广西陆川, 学士, 教授,研究方向为复杂系统优化与智能控制; 孔伟凯(1989-), 男, 河北衡水, 硕士生, 研究方向为智能控制。
  • 基金资助:
    国家自然科学基金(61364007)

Adaptive Dynamic Programming in Raw Meal Fineness Control of Vertical Mill Grinding Process Based on Extreme Learning Machine

Lin Xiaofeng, Kong Weikai   

  1. Guangxi University, Nanning 530004, China
  • Received:2015-04-08 Revised:2015-10-18 Online:2016-11-08 Published:2020-08-13

摘要: 水泥生产中的立磨粉磨过程具有非线性、强耦合、大滞后等特点,对其进行精确的建模和实现生料细度的控制比较困难。提出一种基于极限学习机(ELM, extreme learning machine)的自适应动态规划(ADP, adaptive dynamic programming)优化控制算法。采用极限学习机建立立磨生料粉磨过程的生料细度预测模型,将其作为ADP算法中的模型网络,并以在线序列极限学习机实现ADP的执行网络和评价网络。结果表明:在仿真意义上,所提算法能够对生料细度进行有效地控制,对立磨稳定生产,降低该生产过程的能耗具有一定理论指导意义。

关键词: 水泥立磨, 生料, 自适应动态规划, 极限学习机

Abstract: The grinding process of vertical mill raw meal in cement industry features nonlinear, strong coupling and long time-delay, which is difficult to model precisely and implement stable control for raw meal fineness. Against the problem, a production index prediction model of vertical mill raw meal grinding process was established using Extreme Learning Machine (ELM). Adaptive dynamic programming (ADP) was used to control the raw meal fineness, whose action and critic networks were implemented by online sequential extreme learning machine. In the meaning of simulation, the results show that the proposed method is valid and helpful to reduce the energy consumption.

Key words: vertical mill, raw meal, Adaptive Dynamic Programming (ADP), Extreme Learning Machine (ELM)

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