Adaptive Neural Control Design for Nonlinear Distributed Parameter Systems With Persistent Bounded Disturbances

In this paper, an adaptive neural network (NN) control with a guaranteed L infin -gain performance is proposed for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities and persistent bounded disturbances. Initially, Galerkin method is applied to the PDE system...

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Veröffentlicht in:	IEEE transactions on neural networks Jg. 20; H. 10; S. 1630 - 1644
Hauptverfasser:	WU, Huai-Ning, LI, Han-Xiong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2009 Institute of Electrical and Electronics Engineers
Schlagworte:	Adaptative systems Adaptive control Adaptive systems Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Computer Simulation Connectionism. Neural networks Control design Control nonlinearities Control system analysis Control system synthesis Control systems Control theory. Systems Distributed parameter systems Exact sciences and technology Feedback input-to-state stability (ISS) linear matrix inequality (LMI) Models, Theoretical neural network (NN) Neural networks Neural Networks (Computer) Nonlinear control systems Nonlinear Dynamics Programmable control Temperature control {\cal L}_{\infty} -gain control Differential equation Non linear control Lyapunov method Feedback regulation Control synthesis L Adaptive control Modeling Partial differential equation Optimization Control constraint Parabolic equation Linear matrix inequality neural network (NN) distributed parameter systems Distributed parameter system linear matrix inequality (LMI) Neural network Non linear system Closed feedback Radial basis function Galerkin-Petrov method Neurocontrollers Upper bound Input to state stability input-to-state stability (ISS) Distributed control Derivative gain control Galerkin method Temperature control Lyapunov function
ISSN:	1045-9227, 1941-0093, 1941-0093
Online-Zugang:	Volltext
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