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Indirect adaptive control via parallel dynamic neural networks

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2 Author(s)
Yu, W. ; Seccion de Control Autom., CINVESTAV-IPN, Mexico City, Mexico ; Poznyak, A.S.

Stability conditions for a parallel dynamic neural network by means of Lyapunov-like analysis are determined. The new learning law ensures that the identification error converges to zero (model matching) or to a bounded zone (with unmodelled dynamics). Based on the neural identifier we present a local optimal controller and analyse the tracking error. Our principal contributions are that we provide a bound for the identification error of the parallel neuro identifier and that we then establish a bound for the tracking error of the neurocontrol

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Control Theory and Applications, IEE Proceedings -  (Volume:146 ,  Issue: 1 )