Nonlinear adaptive trajectory tracking using dynamic neural networks

Author

Poznyak, Alexander S. ; Yu, Wen ; Sanchez, Edgar N. ; Perez, Jose P.

Author_Institution

Dept. of Control Autom., CINVESTAV-IPN, Mexico City, Mexico

Volume

10

Issue

6

fYear

1999

fDate

11/1/1999 12:00:00 AM

Firstpage

1402

Lastpage

1411

Abstract

In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error, and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation

Keywords

adaptive control; chaos; identification; neurocontrollers; nonlinear control systems; optimal control; relay control; stability; tracking; Duffing equation; adaptive control; algebraic Riccati equation; chaotic system; differential equations; dynamic neural networks; nonlinear identification; optimal control; second-order relay system; stability conditions; trajectory tracking; Differential algebraic equations; Error analysis; Error correction; Neural networks; Nonlinear equations; Optimal control; Relays; Riccati equations; Stability analysis; Trajectory;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.809085

Filename

809085