Neural networks and feedback linearization

Author

Hassibi, Khosrow M. ; Loparo, Kenneth A.

Author_Institution

Case Western Reserve Univ., Cleveland, OH, USA

fYear

1991

fDate

13-16 Oct 1991

Firstpage

1651

Abstract

The authors propose a general method for learning of the input-output feedback linearization (IOFL) laws for the class of nonlinear systems described by F.-C. Chen (1990). The direct method previously described by the author (1991) is used with some modifications in the implementation. Three general assumptions required for successful implementation of the method are given. The objective was to learn a controller using a high-order three-layer network such that the resulting closed-loop system behaves similarly to a linear reference model. The IOFL problem is classified into three cases, and the required assumptions to learn the feedback for each case are given. In all cases, the feedback linearizable control law is learned directly from the error between the closed-loop system and the reference model outputs

Keywords

closed loop systems; feedback; learning systems; linearisation techniques; neural nets; nonlinear systems; closed-loop system; high-order three-layer network; input-output feedback linearization; learning systems; neural networks; nonlinear systems; reference model outputs; Linear feedback control systems; Linear systems; Manipulator dynamics; Neural networks; Neurofeedback; Nonlinear dynamical systems; Nonlinear systems; Output feedback; State feedback; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on

Conference_Location

Charlottesville, VA

Print_ISBN

0-7803-0233-8

Type

conf

DOI

10.1109/ICSMC.1991.169930

Filename

169930