DocumentCode
1242190
Title
Intelligent Decoupling Control of Nonlinear Multivariable Systems and its Application to a Wind Tunnel System
Author
Fu, Yue ; Chai, Tianyou
Author_Institution
Key Lab. of Integrated Autom. of Process Ind., Northeastern Univ., Shenyang, China
Volume
17
Issue
6
fYear
2009
Firstpage
1376
Lastpage
1384
Abstract
In this paper, for a class of nonlinear multivariable discrete-time systems, an open-loop approximately dynamical decoupling control law is first presented. Then, by introducing a lambda o T difference operator, an intelligent decoupling control method using multiple models and neural networks (NNs) is developed. The intelligent decoupling control method includes a set of fixed decoupling controllers, a reinitialized NN adaptive decoupling controller, a free-running NN adaptive decoupling controller, and a switching mechanism. Theory analysis shows that the free-running NN adaptive decoupling controller can guarantee the bounded-input-bounded-output stability of the closed-loop system, while the multiple fixed decoupling controllers and the reinitialized NN adaptive decoupling controller are used to improve the system performance. To illustrate the method, the proposed design is applied to a 2.4 times 2.4-m injector-driven transonic wind tunnel system. Simulation and industrial experiment results show the effectiveness and practicality of the proposed method.
Keywords
adaptive control; closed loop systems; discrete time systems; multivariable control systems; neurocontrollers; nonlinear control systems; open loop systems; stability; time-varying systems; wind tunnels; adaptive decoupling controller; bounded-input-bounded-output stability; closed-loop system; fixed decoupling controllers; injector-driven transonic wind tunnel system; intelligent decoupling control; neural networks; nonlinear multivariable discrete-time systems; open-loop approximately dynamical decoupling control; 2.4 $times$ 2.4-m wind tunnel; Approximately dynamical decoupling control; intelligent control; multiple models; neural networks (NNs); stability;
fLanguage
English
Journal_Title
Control Systems Technology, IEEE Transactions on
Publisher
ieee
ISSN
1063-6536
Type
jour
DOI
10.1109/TCST.2008.2005487
Filename
4815392
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