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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Published in | Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) Vol. 4; pp. 3536 - 3539 vol.4 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
2001
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Subjects | |
Online Access | Get full text |
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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. |
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ISBN: | 9780780370616 0780370619 |
DOI: | 10.1109/CDC.2001.980407 |