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Planar robot trajectory-tracking using inverse optimal neural control

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3 Author(s)
Tellez, F.O. ; CINVESTAV, Unidad Guadalajara, Mexico ; Sanchez, Edgar N. ; Loukianov, Alexander G.

This paper presents an inverse optimal neural controller, which is constituted by the combination of two techniques: a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to build a model of an assumed unknown nonlinear system. The applicability of the proposed approach is illustrated via simulation by planar robot trajectory tracking.

Published in:

World Automation Congress (WAC), 2010

Date of Conference:

19-23 Sept. 2010