Dynamic neural network-based robust observers for uncertain nonlinear systems
H T Dinh1, R Kamalapurkar2, S Bhasin3
1Department of Mechanical Engineering, University of Transport and Communications, Viet Nam.
Abstract:
A dynamic neural network (DNN) based robust observer for uncertain nonlinear systems is developed. The observer structure consists of a DNN to estimate the system dynamics on-line, a dynamic filter to estimate the unmeasurable state and a sliding mode feedback term to account for modeling errors and exogenous disturbances. The observed states are proven to asymptotically converge to the system states of high-order uncertain nonlinear systems through Lyapunov-based analysis. Simulations and experiments on a two-link robot manipulator are performed to show the effectiveness of the proposed method in comparison to several other state estimation methods.
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