A NN controller and tracking error bound for robotic manipulators

Author

Li, Jinyu ; Wang, Danwei

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

1

fYear

2000

fDate

2000

Firstpage

872

Abstract

In this paper, a robust neural network control scheme is proposed for robot tracking tasks. The neural network is trained online and the weight tuning algorithm has a small dead zone to overcome bounded disturbances. Under this proposed control scheme, it is shown that the tracking error bound is completely determined by the neural network approximation error bound, disturbance bound, as well as the control design parameter. The tracking error bound does not depend on the weight estimation errors. A two-link manipulator is used to illustrate the performance of the control scheme

Keywords

control system synthesis; feedforward neural nets; learning (artificial intelligence); manipulator dynamics; neurocontrollers; real-time systems; robust control; tracking; bounded disturbances; dead zone; dynamics; error bound; multilayer neural nets; neurocontrol; robust control; tracking; two-link manipulator; Adaptive control; Convergence; Error correction; Estimation error; Manipulators; Neural networks; Payloads; Robots; Robust control; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2000. Proceedings of the 39th IEEE Conference on

Conference_Location

Sydney, NSW

ISSN

0191-2216

Print_ISBN

0-7803-6638-7

Type

conf

DOI

10.1109/CDC.2000.912880

Filename

912880