In this paper a scheme based on neural networks for adaptive observation of a class of uncertain continuous nonlinear systems in the presence of time-varying parameters and non-vanishing disturbances is proposed. Using standard Lyapunov procedures and an adaptive bounding technique, the state error convergence to zero is proved, even when approximation error and disturbances are present, while guaranteeing uniform ultimate boundedness of all others estimation errors (weight, parameter and bounding function). A simulation example to illustrate the application and performance of the proposed algorithm is provided.
Adaptive observers; nonlinear systems; neural networks; Lyapunov methods
