Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/40700
Title: Discrete-time decentralized inverse optimal neural control for a shrimp robot
Author: Lopez-Franco, M.
Sanchez, E.N.
Alanis, A.Y.
Lopez-Franco, C.
Arana-Daniel, N.
Issue Date: 2014
Abstract: Many sophisticated analytical procedures for control design are based on the assumption that the full state vector is available for measurement. When this is not the case, is required an observer. In this paper, the super-twisitng second-order sliding-mode algorithm is modified in order to design an observer for the actuators; then a recurrent high order neural network (RHONN) is used to identify the plant model, under the assumption of all the state is available for measurement. The learning algorithm for the RHONN is implemented using an Extended Kalman Filter (EKF) algorithm. On the basis of the identifier a controller which uses inverse optimal control, is designed to avoid solving the Hamilton Jacobi Bellman (HJB) equation. The proposed scheme is implemented in discrete-time to control a KUKA youBot. " 2014 TSI Press.",,,,,,"10.1109/WAC.2014.6936014",,,"http://hdl.handle.net/20.500.12104/40700","http://www.scopus.com/inward/record.url?eid=2-s2.0-84908891847&partnerID=40&md5=ffd72c0d4175e437c3e6604d209c552d",,,,,,,,"World Automation Congress Proceedings",,"496
501",,,,"Scopus",,,,,,,,,,,,"Discrete-time decentralized inverse optimal neural control combined with sliding mode for mobile robots",,"Conference Paper" "42480","123456789/35008",,"Lopez-Franco, M., CINVESTAV, Unidad Guadalajara, Jalisco 45015, Mexico; Sanchez, E.N., CINVESTAV, Unidad Guadalajara, Jalisco 45015, Mexico; Alanis, A.Y., CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico; Arana-Daniel, N., CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico",,"Lopez-Franco, M.
Sanchez, E.N.
Alanis, A.Y.
Arana-Daniel, N.",,"2013",,"This paper deals with an decentralized inverse optimal neural controller for discrete-time unknown nonlinear systems, in presence of external disturbances and parameter uncertainties. It is based on two techniques: first, an identifier using a discrete-time recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm; second, a controller which on the basis of inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation. Computer simulations are presented which illustrate the effectiveness of the proposed tracking control law. " 2013 AACC American Automatic Control Council.
URI: http://hdl.handle.net/20.500.12104/40701
http://www.scopus.com/inward/record.url?eid=2-s2.0-84883533090&partnerID=40&md5=f5e9f153e87a1ad07f43b17201e84c2c
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