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中南大学学报(自然科学版)

Journal of Central South University

第47卷    第8期    总第264期    2016年8月

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文章编号:1672-7207(2016)08-2676-09
基于线性函数型权重的RBF-ARX模型的磁悬浮球系统预测控制
覃业梅1, 2, 3,彭辉1, 3, 4,阮文杰1, 3

(1. 中南大学 信息科学与工程学院,湖南 长沙,410083;
2. 湖南商学院 计算机与信息工程学院,湖南 长沙,410205;
3. 先进控制与智能自动化湖南省工程实验室,湖南 长沙,410083;
4. 两型社会与生态文明协同创新中心,湖南 长沙,410083
)

摘 要: 为了充分描述磁悬浮球系统具有非线性、开环不稳定性及响应快速性等特性,建立一个带线性函数权重的RBF-ARX(linear functional weight RBF networks-based ARX model, LFWRBF-ARX)模型。与一般的RBF-ARX模型不同之处在于,它引入1个与工作点状态相关的局部线性结构作为RBF网络输出层的权值。该模型随系统工作点的变化而变化,固定工作点时为局部线性ARX模型,当工作点变化时为全局非线性ARX模型。根据该模型的结构特点,采用结构化非线性参数优化方法(structured nonlinear parameter optimization method, SNPOM)来辨识模型的结构及线性、非线性参数。然后,以辨识的模型为基础,根据模型的局部线性及全局非线性特征设计预测控制器。仿真结果表明:以该建模方法建立的模型能很好地局部和全局描述磁悬浮球系统的动态特性,并能实现小球的稳定悬浮控制,比以一般ARX模型、RBF-ARX模型为基础的控制效果更好。

 

关键词: 非线性ARX模型(NARX);线性函数权重RBF-ARX模型(LFWRBF-ARX);SNPOM(structured nonlinear parameter optimization method);非线性预测控制;磁悬浮球

Modeling and predictive control of magnetic levitation ball system based on RBF-ARX model with linear functional weights
QIN Yemei1, 2, 3, PENG Hui1, 3, 4, RUAN Wenjie1, 3

1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. School of Computer and Information Engineering, Hunan University of Commerce, Changsha 410205, China;
3. Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Changsha 410083, China;
4. Collaborative Innovation Center of Resource-Conserving &Environment-Friendly Society and Ecological Civilization, Changsha 410083, China

Abstract:In order to fully describe the dynamic behavior of the magnetic levitation ball system which has nonlinear, open-loop instable and rapid response characteristics, RBF-ARX model with linear functional weights (LFWRBF-ARX model) was established. Different from a general RBF-ARX model, the LFWRBF-ARX model introduces a local linear structure as the weights of output layer. This model varies with the working-point, it is a locally linear ARX model when the working-point is fixed and it becomes a globally nonlinear ARX model when the working-point changes. According to the model structure, a structured nonlinear parameter optimization method (SNPOM) was applied to identify the structure, linear and nonlinear parameters of model. Then the identified model-based predictive controller was designed. The results show that the LFWRBF-ARX model may capture the local and global dynamic characteristics of the magnetic levitation ball system well, and the ball may be controlled to levitate stably. The control results are better than those of the general ARX model and RBF-ARX model-based control.

 

Key words: nonlinear ARX model; RBF-ARX model with linear functional weights (LFWRBF-ARX); SNPOM; nonlinear model predictive control; magnetic levitation (maglev) system

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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