Output tracking with constrained inputs via inverse optimal adaptive recurrent neural control

This paper extends previous results to the output tracking problem of nonlinear systems with unmodelled dynamics and constrained inputs. A recurrent high order neural network is used to identify the unknown system dynamics and a learning law is obtained using the Lyapunov methodology. A stabilizing control law for the output tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law for nonlinear systems with constrained inputs.

[1]  Prodromos Daoutidis,et al.  Stabilization of unstable systems with input constraints , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[2]  Manolis A. Christodoulou,et al.  Dynamical Neural Networks that Ensure Exponential Identification Error Convergence , 1997, Neural Networks.

[3]  Kay Soon Low,et al.  Robust model predictive control of a motor drive with control input constraints , 2003, The Fifth International Conference on Power Electronics and Drive Systems, 2003. PEDS 2003..

[4]  Yuandan Lin,et al.  A universal formula for stabilization with bounded controls , 1991 .

[5]  Xinghuo Yu,et al.  Chaos control : theory and applications , 2003 .

[6]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[7]  Anthony J. Calise,et al.  Dynamic neural networks for output feedback control , 2001 .

[8]  Miroslav Krstic,et al.  Stabilization of Nonlinear Uncertain Systems , 1998 .

[9]  Li Qiu,et al.  Stabilization of linear systems with input constraints , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[10]  Edgar N. Sánchez,et al.  Chaos control and synchronization, with input saturation, via recurrent neural networks , 2003, Neural Networks.

[11]  N. El‐Farra,et al.  Integrating robustness, optimality and constraints in control of nonlinear processes , 2001 .

[12]  Edgar N. Sanchez,et al.  RECURRENT NEURAL CONTROL FOR ROBOT TRAJECTORY TRACKING , 2002 .

[13]  Manolis A. Christodoulou,et al.  Adaptive Control with Recurrent High-order Neural Networks , 2000, Advances in Industrial Control.

[14]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Randal W. Beard,et al.  CLF-based tracking control for UAV kinematic models with saturation constraints , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[16]  Anuradha M. Annaswamy,et al.  Stable Adaptive Systems , 1989 .

[17]  Tamer Başar,et al.  H1-Optimal Control and Related Minimax Design Problems , 1995 .