In this work, based on the neural network and feedback linearization techniques, a novel method to design robust control for a class of MIMO discretetime nonlinear uncertain systems is proposed. This method includes four different control schemes, which can be applied depending on the state vector measurement viability:
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a)
The first designed robust direct neural control scheme is based on the backstepping technique, approximated by a high order neural network. On the basis of the Lyapunov approach, the respective stability analysis, for the whole closed-loop system, including the extended Kalman filter (EKF)-based NN learning algorithm, is also performed.
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b)
The second robust indirect control is designed with a recurrent high order neural network, which enables to identify the plant model. A strategy to avoid specific adaptive weights zero-crossing and conserve the identifier controllability property is proposed. Based on this neural identifier and applying the discrete-time block control approach, a nonlinear sliding manifold with a desired asymptotically stable motions was formulated. Using a Lyapunov functions approach, a discrete-time sliding mode control that makes the designed sliding manifold to be attractive was introduced.
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Keywords
- Induction Motor
- Extended Kalman Filter
- Nonlinear Uncertain System
- Backstepping Technique
- Lyapunov Approach
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© 2008 Springer-Verlag Berlin Heidelberg
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(2008). Conclusions and Future Work. In: Discrete-Time High Order Neural Control. Studies in Computational Intelligence, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78289-6_8
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DOI: https://doi.org/10.1007/978-3-540-78289-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78288-9
Online ISBN: 978-3-540-78289-6
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