Title :
Mechanical systems tracking using neural networks and state estimation simultaneously
Author :
León, JesÙs De ; Sànchez, Edgar N. ; Chataigner, Adeline
Author_Institution :
Univ. Autonoma de Nuevo Leon, San Nicolas de los Garza, Mexico
Abstract :
In this paper, we analyze how to implement a nonlinear observer, which combined with a structured neural network, allows tracking of an artificial output, even when the state is not fully measurable; as an effect of this tracking, the system state converges to any selected equilibrium point. We establish and prove a theorem about the stability of the tracking error and of the state estimation error. The applicability of the approach is illustrated by simulation results
Keywords :
convergence; neural nets; nonlinear systems; observers; stability; mechanical systems tracking; nonlinear observer; stability; state estimation error; structured neural network; system state convergence; tracking error; Artificial neural networks; Control systems; Force control; Linear systems; Matrices; Mechanical systems; Neural networks; Nonlinear control systems; Nonlinear systems; State estimation;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.410894
