Adaptive dynamic neural-network observer design of velocity feedbacks

In this paper, an adaptive dynamic neural-network observer is designed for unknown or uncertain nonlinear systems and utilized to estimate unmeasurable states. The contributions of paper are in twofold. First, using variable learning rate and internally stable neurons, convergence of parameters is guaranteed and overall stable adaptive observer is designed. Second, designed observer is applied to a real-time flexible-link transmission system data with unmodeled dynamics where the velocities could not be estimated using approximate mathematical model. The SPR condition of the adaptive observer has been satisfied via output error filtering. The boundedness of the estimation error and other signals has been shown using Lyapunov stability. The application results are presented to demonstrate the applicability and efficacy of the designed observer.