Chaos synchronization via adaptive recurrent neural control

This paper proposes a new adaptive control structure, based on a dynamic neural network, for trajectory tracking of unknown nonlinear plants. The main components of this structure include a neural identifier and a control law, which together guarantee the desired trajectory tracking performance. Sta...

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Bibliographic Details
Published inProceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) Vol. 4; pp. 3536 - 3539 vol.4
Main Authors Sanchez, E.N., Perez, J.P., Ricalde, L.J., Guanrong Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 2001
Subjects
Adaptive control
Analytical models
Chaos
Control system analysis
Lyapunov method
Neural networks
Programmable control
Stability analysis
System testing
Trajectory
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Summary:This paper proposes a new adaptive control structure, based on a dynamic neural network, for trajectory tracking of unknown nonlinear plants. The main components of this structure include a neural identifier and a control law, which together guarantee the desired trajectory tracking performance. Stability of the tracking control is analyzed by using the Lyapunov function method, and the structure is tested by simulations on an example of complex dynamical systems: chaos synchronization.
ISBN:9780780370616
0780370619
DOI:10.1109/CDC.2001.980407