系统仿真学报 ›› 2020, Vol. 32 ›› Issue (5): 837-847.doi: 10.16182/j.issn1004731x.joss.18-0527

• 仿真模型/系统置信度评估技术 • 上一篇    下一篇

改进的逆解耦自抗扰内模控制在磨矿中的应用

周颖, 贾巧娟, 张燕, 常明新   

  1. 河北工业大学人工智能与数据科学学院,天津 300130
  • 收稿日期:2018-07-30 修回日期:2018-10-31 出版日期:2020-05-18 发布日期:2020-05-15
  • 作者简介:周颖(1971-),女,河北,博士,副教授,研究方向为智能控制与模式识别。
  • 基金资助:
    国家自然科学基金(61741307),河北省教育厅重点项目(ZD2016071),河北省自然科学基金(F2018202279)

Application of Improved Inverse Decoupling Active Disturbance Rejection Internal Model Control in Grinding

Zhou Ying, Jia Qiaojuan, Zhang Yan, Chang Mingxin   

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China
  • Received:2018-07-30 Revised:2018-10-31 Online:2020-05-18 Published:2020-05-15

摘要: 针对磨矿分级系统的多变量、强耦合、大时滞等特性,提出改进的逆解耦自抗扰内模控制方法。利用逆解耦方法实现磨矿分级系统的解耦,对解耦后的子系统采用改进的内模控制和线性自抗扰控制。通过引入内模补偿器和增益对时滞进行补偿,减小了系统对模型的依赖度,通过调节线性自抗扰控制器参数、内模补偿器参数和增益来抑制模型失配、外部干扰及不确定性因素对系统带来的不利影响。仿真结果表明,改进的逆解耦自抗扰内模控制方法有较好的解耦性能、跟踪性能和鲁棒性能,验证了此方法是有效的。

关键词: 磨矿分级系统, 逆解耦, 线性自抗扰控制, 内模控制

Abstract: Aiming at the characteristics of the multivariable, strong coupling and large time delay of the grinding classification system, the improved inverse decoupling active disturbance rejection internal model control method is proposed. The inverse decoupling method is used to realize the decoupling of the grinding classification system, and the improved internal model control and linear active disturbance rejection control are adopted for the decoupled subsystem. By introducing the internal model compensator and the gain to compensate the time delay, the dependence of the system upon the model is reduced .The model mismatch, external disturbance and uncertainties are suppressed by adjusting the parameters of the linear active disturbance rejection controller, the internal model compensator and gain. The simulation results show that the improved inverse decoupling active disturbance rejection internal model control method has the better decoupling performance, tracking performance and robust performance. The method is proved to be effective.

Key words: grinding classification system, inverted decoupling, linear active disturbance rejection control, internal model control

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