基于模糊神经网络的MIMO系统自适应解耦控制

白辰; 樊垚; 任章; 杨鹏

doi:10.13700/j.bh.1001-5965.2014.0758

基于模糊神经网络的MIMO系统自适应解耦控制

doi: 10.13700/j.bh.1001-5965.2014.0758

北京航空航天大学自动化科学与电气工程学院, 北京 100191

基金项目: 国家自然科学基金(91116002,91216304,61333011,61121003)

详细信息

作者简介:
白辰(1988-),男,河南焦作人,博士研究生,buaabaichen@hotmail.com

通讯作者:
任章(1957-),男,河南濮阳人,教授,renzhang@buaa.edu.cn,主要研究方向为导航制导与控制.

中图分类号: TP273
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- 被引次数: 0
出版历程
- 收稿日期: 2014-12-03
- 修回日期: 2015-03-13
- 网络出版日期: 2015-11-20

Adaptive decoupling control of a MIMO system based on fuzzy neural networks

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 针对一类具有不确定性的多输入多输出(MIMO)非线性系统控制问题,提出了基于模糊神经网络的自适应解耦控制方法.根据分散控制理论和反馈线性化方法设计了MIMO非线性系统的分通道解耦控制律,然后把通道耦合项和不确定性项归结为总的系统扰动项,利用模糊神经网络观测器得到其估计值,并作为补偿信号加入到解耦控制律中.证明了所设计的解耦控制律、模糊神经网络观测器以及模糊神经网络权值向量自适应律可以保证控制误差、扰动估计误差和权值向量误差一致最终收敛.仿真中将本文的方法与传统的输出反馈控制律进行了对比,结果表明加入的补偿控制信号消除了通道耦合和不确定性带来的不利影响,验证了该方法的有效性和稳定性.
- 多输入多输出系统 /
- 模糊神经网络 /
- 自适应 /
- 解耦控制 /
- 不确定性
Abstract: According to the control problem of a class of uncertain multiple-input multiple-output (MIMO) nonlinear systems, an adaptive decoupling control approach based on fuzzy neural networks was proposed. Firstly, a sub-channel decoupling control law of MIMO nonlinear systems was designed using decentralized control theory and feedback linearization approach. Secondly, the approximation of the system coupling terms and uncertainty terms were obtained by a fuzzy neural networks observer and compensated into the control law as compensation signal. It was proved that the control law, the observer and the weighted vector adaptive law could guarantee the uniform convergence of the errors of the output variable, the observer variable and the weighted vector finally. Simulations were carried on a typical uncertain MIMO system. The proposed method was compared with a traditional output feedback control method without adding compensation control signal. The simulation results show that the influence caused by coupling among the channels and uncertainty is eliminated by the compensation control and the observer errors can converge. The results validate the effectiveness and stability of the proposed control approach.
- multiple-input multiple-output systems /
- fuzzy neural networks /
- adaptive /
- decoupling control /
- uncertainty

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