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Nonlinear adaptive trajectory tracking using dynamic neural networks

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4 Author(s)
A. S. Poznyak ; Dept. of Control Autom., CINVESTAV-IPN, Mexico City, Mexico ; Wen Yu ; E. N. Sanchez ; J. P. Perez

In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error, and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation

Published in:

IEEE Transactions on Neural Networks  (Volume:10 ,  Issue: 6 )