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Decentralized adaptive control of nonlinear systems using radial basis neural networks

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2 Author(s)
Spooner, J.T. ; Dept. of Control Subsyst., Sandia Nat. Labs., Albuquerque, NM, USA ; Passino, K.M.

Stable direct and indirect decentralized adaptive radial basis neural network controllers are presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Due to the functional approximation capabilities of radial basis neural networks, the dynamics for each subsystem are not required to be linear in a set of unknown coefficients as is typically required in decentralized adaptive schemes. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds

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Automatic Control, IEEE Transactions on  (Volume:44 ,  Issue: 11 )