Direct neural network-based self-tuning control for a class of nonlinear systems

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Original languageEnglish
Pages (from-to)623-641
Journal / PublicationInternational Journal of Systems Science
Issue number8
Publication statusPublished - Jan 2007


Most self-tuning control algorithms for nonlinear systems become invalid when the controlled systems have nonminimum phase property. In this article, a direct neural network-based self-tuning control strategy is developed to deal with this problem under the certainty equivalence principle. Based on an equivalent linearized model from the local linearization, the controller structure is designed using a modified Clarke index with the guaranteed closed-loop stability and without the traditional requirement of the globally boundedness. For the system with unknown parameters, the controller is self-tuned by an on line RBF neural network identifier. Satisfactory simulations illustrate the effectiveness and adaptability of the proposed strategy even under system parameter variations.

Research Area(s)

  • Local linearization, Neural network, Nonlinear systems, Nonminimum phase property, Self-tuning control