Self-tuning predictive control applicable to ship magnetic levitation damping device

IF 2.5 Q2 ENGINEERING, INDUSTRIAL
Hui Zhang, Jinghao Yan, Weiran Wang, Meng Xu, Wenjing Ma
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引用次数: 1

Abstract

In the ship design, there are strict vibration-proof requirements for precision instruments. Therefore, a ship repulsive magnetic levitation damping device is designed to achieve vibration reduction. And one self-tuning predictive control method is proposed to achieve the stable levitation of this device. Firstly, a predictive control (MPC) method with state constraints and input constraints is adopted to realise the stable suspension of the floater. The MPC can solve the problem of position imbalance of the magnetic levitation system under the external complex disturbances. Secondly, a self-tuning MPC method based on recursive least square is proposed to solve the problem caused by the fixed parameters of the traditional predictive controller. At the beginning of each control cycle, the recursive least-squares (RLS) method is used to estimate the parameters of the system. Thus, the optimal control model could be obtained for the current situation. Then, this model is applied to the predictive controller to solve the problem of parameter fixation in the traditional predictive control. Finally, the simulation results show that it can improve the accuracy, dynamic response and anti-interference performance obviously.

Abstract Image

适用于船舶磁悬浮阻尼装置的自校正预测控制
在船舶设计中,对精密仪器有严格的防震要求。为此,设计了船舶排斥性磁悬浮减振装置,以达到减振的目的。为实现该装置的稳定悬浮,提出了一种自整定预测控制方法。首先,采用状态约束和输入约束相结合的预测控制方法实现浮子的稳定悬浮;MPC可以解决磁悬浮系统在外部复杂扰动下的位置不平衡问题。其次,针对传统预测控制器参数固定的问题,提出了一种基于递推最小二乘的自整定MPC方法。在每个控制周期开始时,采用递推最小二乘(RLS)方法估计系统参数。从而得到当前情况下的最优控制模型。然后,将该模型应用到预测控制器中,解决了传统预测控制中参数固定的问题。仿真结果表明,该方法能明显提高系统的精度、动态响应和抗干扰性能。
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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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