Neural-based adaptive control design for general nonlinear systems and its application to process control

Author

Ge, S.S. ; Hang, C.C. ; Zhang, T.

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

1

fYear

1998

fDate

21-26 Jun 1998

Firstpage

73

Abstract

In this work, a neural-based adaptive controller is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The proposed controller ensures that the output tracking error converges to a small neighborhood of the origin. The weight updating law of neural networks (NNs) is derived using Lyapunov theory and the stability of the closed-loop system is guaranteed. The proposed control scheme has been successfully applied to the composition control in a continuously stirred tank reactor (CSTR) in chemical processes

Keywords

Lyapunov methods; adaptive control; closed loop systems; control system synthesis; neurocontrollers; nonlinear control systems; process control; stability; uncertain systems; CSTR; Lyapunov theory; chemical processes; closed-loop system stability; composition control; continuously stirred tank reactor; general nonlinear systems; neural-based adaptive control design; output tracking error convergence; process control; tracking control problem; weight updating law; Adaptive control; Continuous-stirred tank reactor; Control systems; Error correction; Inductors; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1998. Proceedings of the 1998

Conference_Location

Philadelphia, PA

ISSN

0743-1619

Print_ISBN

0-7803-4530-4

Type

conf

DOI

10.1109/ACC.1998.694631

Filename

694631