Nonlinear systems identification and control using dynamic multi-time scales neural networks

Author

Han, Xuan ; Xie, Wen-Fang

Author_Institution

Dept. of Mech. & Ind. Eng., Concordia Univ., Montreal, QC, Canada

fYear

2009

fDate

5-7 Aug. 2009

Firstpage

96

Lastpage

101

Abstract

An on-line identification algorithm via dynamic neural networks with different time-scales followed by controller design is proposed for the dynamic systems with nonlinearity and uncertainty in this paper. The main contribution of the paper is that the Lyapunov function analysis, singularly perturbed technique and sliding mode methodology are combined to develop the control laws for trajectory tracking with consideration of the modeling error and disturbance. Simulations are given to demonstrate the effectiveness of the theoretical results.

Keywords

Lyapunov methods; adaptive control; control nonlinearities; control system analysis; control system synthesis; identification; neurocontrollers; nonlinear control systems; position control; singularly perturbed systems; uncertain systems; variable structure systems; Lyapunov function analysis; adaptive control; control nonlinearities; dynamic neural network; modeling disturbance; modeling error; multitime scale; nonlinear system; online identification algorithm; singularly perturbed technique; sliding mode methodology; trajectory tracking; uncertain system; Algorithm design and analysis; Control systems; Heuristic algorithms; Lyapunov method; Neural networks; Nonlinear control systems; Nonlinear systems; Sliding mode control; Trajectory; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2009. ICAL '09. IEEE International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-4794-7

Electronic_ISBN

978-1-4244-4795-4

Type

conf

DOI

10.1109/ICAL.2009.5262970

Filename

5262970