Trajectory tracking based on differential neural networks for a class of nonlinear systems

J. Humberto Pérez-Cruz, Alexander Poznyak

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

A very successful scheme to accomplish trajectory tracking of unknown nonlinear systems consists of identifying the unknown dynamics using differential neural networks and on the basis of the so obtained mathematical model to develop an appropriate control law. The purpose of this paper is to present some new results in this sense. In particular, for the neural identifier, a new online learning law which permits to guarantee the boundedness for both the weights and the identification error without using a dead zone function is showed. Likewise, based on this neural identifier, a new control law to guarantee the boundedness of the tracking error is developed. These results are proved using a Lyapunov like analysis. With respect to the approach based on the local optimal control theory, the new approach has a similar performance but its main advantage consists of simplifying considerably the design process. The workability of the suggested approach is illustrated by simulation.

Idioma originalInglés
Título de la publicación alojada2009 American Control Conference, ACC 2009
Páginas2940-2945
Número de páginas6
DOI
EstadoPublicada - 2009
Publicado de forma externa
Evento2009 American Control Conference, ACC 2009 - St. Louis, MO, Estados Unidos
Duración: 10 jun. 200912 jun. 2009

Serie de la publicación

NombreProceedings of the American Control Conference
ISSN (versión impresa)0743-1619

Conferencia

Conferencia2009 American Control Conference, ACC 2009
País/TerritorioEstados Unidos
CiudadSt. Louis, MO
Período10/06/0912/06/09

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