Robust telemanipulator control using a partitioned neural network architecture

Author

Tzafestas, Spyros G. ; Prokopiou, Platon A. ; Tzafestas, Costas S.

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece

Volume

3

fYear

1997

fDate

9-12 Jun 1997

Firstpage

1755

Abstract

In this paper the control problem of telemanipulators is considered under the condition that they are subject to modeling and other uncertainties of considerable levels. The design is based on the S. Lee and H.S. Lee teleoperator control scheme (1993, 1994), which is modified so as to be able to compensate the uncertainties, and is implemented using a partitioned multilayer perceptron neural network. Several subnetworks are used each one identifying a term of the manipulator´s dynamic model. A new learning algorithm is proposed which distributes the learning error to each subnetwork and enables online training. Several simulation results are provided, which show the robustness ability by the partitioned neurocontroller, and compare it with the results obtained through sliding mode control

Keywords

learning (artificial intelligence); manipulator dynamics; multilayer perceptrons; neural net architecture; neurocontrollers; robust control; telerobotics; manipulator dynamic model identification; partitioned multilayer perceptron neural network; partitioned neural network architecture; partitioned neurocontroller; robust telemanipulator control; sliding mode control; uncertainty compensation; Manipulator dynamics; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurocontrollers; Partitioning algorithms; Robust control; Sliding mode control; Teleoperators; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614161

Filename

614161