基于切换机制的多模型自适应混合控制

曹叙风; 王昕; 王振雷

doi:10.16383/j.aas.2017.c150698

基于切换机制的多模型自适应混合控制

doi: 10.16383/j.aas.2017.c150698

曹叙风^1,,
王昕^2,,
王振雷^1, ,

1.
华东理工大学化工过程先进控制和优化技术教育部重点实验室上海 200237
2.
上海交通大学电工与电子技术中心上海 200240

基金项目:

上海市自然科学基金 13ZR1411300, 14ZR1410000, 14ZR1421800

流程工业综合自动化国家重点实验室开放课题基金 PAL-N201404

国家自然科学基金 61673268, 61333010, 61533012

详细信息

作者简介:
曹叙风华东理工大学信息科学与工程学院硕士研究生.主要研究方向为多模型控制.E-mail:cao.xufeng@sfit.shfe.com.cn

王昕上海交通大学副教授.主要研究方向为多模型自适应控制,多变量智能解耦控制,复杂工业过程建模、控制与优化.E-mail:wangxin26@sjtu.edu.cn

通讯作者:
王振雷华东理工大学教授.主要研究方向为智能控制,复杂系统的建模及特征分析,故障诊断和智能优化算法.本文通信作者. E-mail:wangzhen_l@ecust.edu.cn

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- 文章访问数: 2712
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- 被引次数: 21
出版历程
- 收稿日期: 2015-10-26
- 录用日期: 2016-02-26
- 刊出日期: 2017-01-01

Multiple Model Adaptive Mixing Control Based on Switching

CAO Xu-Feng^1
,,
WANG Xin^2
,,
WANG Zhen-Lei^{1
, ,}

1.
Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237
2.
Center of Electrical&Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240

Funds:

Natural Science Foundation of Shanghai 13ZR1411300, 14ZR1410000, 14ZR1421800

State Key Laboratory of Synthetical Automation for Process Industries PAL-N201404

Supported by National Natural Science Foundation of China 61673268, 61333010, 61533012

More Information

Author Bio:
CAO Xu-feng Master student at the School of Information Science and Engineering, East China University of Science and Technology. His main re-search interest is multiple model control.E-mail:

WANG Xin Associate professor at Shanghai Jiao Tong University. His re-search interest covers multiple models adaptive control, multivariable intelli-gent decoupling control, modeling, control and optimiza-tion of complex industrial processes.E-mail:

Corresponding author: WANG Zhen-Lei Professor at East China University of Science and Technology. His research interest cov-ers intelligent control, modeling and analysis the charac-teristics, fault diagnosis, and intelligent optimization algo-rithms of complex systems. Corresponding author of this paper.. E-mail:wangzhen_l@ecust.edu.cn

摘要

摘要: 针对多模型自适应混合控制的性能依赖系统参数估计误差大小的缺点，本文提出了基于切换机制的多模型自适应混合控制.首先对被控系统进行辨识，然后根据参数估计值进行判断.当参数估计值不在最优参数集内时，实行切换策略，重置参数估计值到最优参数集内，用以减小暂态误差，提高暂态性能；当参数估计值在最优参数集内时，实行混合控制，用以平滑过渡过程.文中给出了系统的稳定性和收敛性的证明，最后的仿真实验结果验证了所提出方法的可行性.
- 多模型 /
- 自适应 /
- 混合控制 /
- 切换
Abstract: As the performance of the multiple model adaptive mixing control depends on the error of parameter estimates, a multiple model adaptive mixing control based on switching is proposed in this paper. The system is identified first and parameter estimates are obtained. When the parameter estimates are not in the optimal parameter set, switching control is applied to reset the parameter estimates in the optimal parameter set. As a result, the transient error gets smaller and the transient performance improves. When the parameter estimates are in the optimal parameter set, mixing control is used to smooth the transition process. In addition, the analysis of the stability and convergence is presented. Finally, the feasibility of the method is verified in the simulation.
- Multiple models /
- adaptive /
- mixing control /
- switching control

HTML全文

图 1 b₁(0) = 0:4 时系统的输出

Fig. 1 The output of system when b₁(0) = 0:4

下载: 全尺寸图片幻灯片

图 2 参数初始值理想时, AMC 方法的混合信号

Fig. 2 The mixing signal of AMC when the initial value of parameter estimate is ideal

下载: 全尺寸图片幻灯片

图 3 中文参数初始值理想时, AMC-S 方法的混合信号标题

Fig. 3 The mixing signal of AMC-S when the initial value of parameter estimate is ideal

下载: 全尺寸图片幻灯片

图 4 b₁(0) =-0:4 时系统的输出

Fig. 4 The output of system when b₁(0) =-0:4

下载: 全尺寸图片幻灯片

图 5 AMC 方法的混合信号

Fig. 5 The mixing signal of AMC

下载: 全尺寸图片幻灯片

图 6 AMC-S 方法的混合信号

Fig. 6 The mixing signal of AMC-S

下载: 全尺寸图片幻灯片

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