Output feedback control of nonlinear systems using RBF neural networks

Author

Seshagiri, Sridhar ; Khalil, Hassan K.

Author_Institution

Sci. Res. Lab., Ford Motor Co., Dearborn, MI, USA

Volume

11

Issue

1

fYear

2000

fDate

1/1/2000 12:00:00 AM

Firstpage

69

Lastpage

79

Abstract

An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimate boundedness. The effectiveness of the proposed method is demonstrated through simulations. The simulations also show that by using adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower order networks

Keywords

adaptive control; feedback; nonlinear systems; radial basis function networks; robust control; Lyapunov-based design; RBF neural networks; adaptive output feedback control; control saturation; high-gain observer; lower order networks; nonlinear systems; output feedback control; output tracking; parameter projection; plant nonlinearities; uniform ultimate boundedness; Adaptive control; Approximation error; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Output feedback; Programmable control; Radial basis function networks; Robust control;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.822511

Filename

822511