Title :
Trajectory tracking via adaptive recurrent control with input saturation
Author :
Sanchez, Edgar N. ; Ricalde, Luis J.
Author_Institution :
CINVESTAV, Unidad Guadalajara, Mexico
Abstract :
This paper deals with the adaptive tracking problem for nonlinear systems in presence of unknown parameters, unmodelled dynamics and input saturation. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then stabilizing control law for the reference tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law. Tracking error boundedness is established as a function of a design parameter.
Keywords :
Lyapunov methods; adaptive control; control nonlinearities; neurocontrollers; nonlinear control systems; position control; recurrent neural nets; stability; Lyapunov methodology; Sontag control law; adaptive recurrent control; high order recurrent neural network; input saturation; nonlinear systems; reference tracking error dynamics; stability; tracking error boundedness; trajectory tracking; Adaptive control; Control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Recurrent neural networks; Stability; Trajectory;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223372
