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RECURRENT NEURAL CONTROL FOR ROBOT TRAJECTORY TRACKING
Edgar N. Sanchez* Jose P. Perez** Luis J. Ricalde*
* CINVESTAV, Unidad Guadalajara, Apartado Postal 31-430,
Plaza La Luna, Guadalajara, Jalisco C. P. 45091, Mexico, e-mail:
sanchez@gdl.cinvestav.mx
** CINVESTAV, Unidad Guadalajara, on doctoral studies leave
from, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo
Leon, Mexico
This paper extends the results previously obtained for trajectory tracking of unknown plants using recurrent neural networks. The proposed controller structure is composed of a neural identifier and a control law defined by using the inverse optimal control approach, which has been improved so that less inputs than states are needed. The proposed new control scheme is applied to the control a robotic manipulator model.
Keywords: Neural networks, Trajectory tracking, Adaptive control, Lyapunov function, Stability analysis
Session slot T-Th-M04: Neuro control systems/Area code 3e : Fuzzy and Neural Systems
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