Neural network based adaptive control of a flexible link manipulator

Author

Mahmood, Niaz ; Walcott, Bruce L.

Author_Institution

Dept. of Electr. Eng., Kentucky Univ., Lexington, KY, USA

fYear

1993

fDate

24-28 May 1993

Firstpage

851

Abstract

This paper presents a design methodology for an on-line self-tuning adaptive control (OLSTAC) of a single flexible link manipulator (FLM) using backpropagation neural networks (BPNN). The particular problem discussed is the on-line system identification of a FLM using BPNN and the OLSTAC of a FLM using a separate neural network as a controller. A finite-element model of a FLM is obtained using ANSYS. The pseudo-link concepts developed in [2] are used to determine on-line angular displacement of the end effector of the FLM. The illustrative simulation results are promising and show that the OLSTAC technique can be applied to flexible structures such as a FEM resulting reduced error and increased robustness

Keywords

backpropagation; control system synthesis; digital simulation; identification; manipulators; neural nets; self-adjusting systems; stability; adaptive control; backpropagation neural networks; end effector; finite-element model; flexible link manipulator; flexible structures; identification; neural network; online angular displacement; online self-tuning adaptive control; pseudo-link; reduced error; robustness; simulation; Adaptive control; Backpropagation; Control systems; Design methodology; End effectors; Finite element methods; Flexible structures; Neural networks; Robustness; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace and Electronics Conference, 1993. NAECON 1993., Proceedings of the IEEE 1993 National

Conference_Location

Dayton, OH

Print_ISBN

0-7803-1295-3

Type

conf

DOI

10.1109/NAECON.1993.290831

Filename

290831