An application of Gaussian radial based function neural networks for the control of a nonlinear multi link robotic manipulator

Author

Robert T Mizerek, University of Nevada, Las Vegas

Award Date

1-1-1994

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering

Number of Pages

77

Abstract

The theory of Gaussian radial based function neural networks is developed along with a stable adaptive weight training law founded upon Lyapunov stability theory. This is applied to the control of a nonlinear multi-linked robotic manipulator for the general case of N links. Simulations of a two link system are performed and demonstrate the derived principles.

Keywords

Application; Function; Gaussian; Manipulator; Networks; Neural; Radial; Robotic

Controlled Subject

Electrical engineering; Artificial intelligence

File Format

pdf

File Size

2181.12 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

If you are the rightful copyright holder of this dissertation or thesis and wish to have the full text removed from Digital Scholarship@UNLV, please submit a request to digitalscholarship@unlv.edu and include clear identification of the work, preferably with URL.

Repository Citation

Mizerek, Robert T, "An application of Gaussian radial based function neural networks for the control of a nonlinear multi link robotic manipulator" (1994). UNLV Retrospective Theses & Dissertations. 385.
http://dx.doi.org/10.25669/2uej-t36q

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/