Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
406189 | Neural Networks | 2014 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
H.T. Dinh, R. Kamalapurkar, S. Bhasin, W.E. Dixon,