Tracking improvement for stable robot control using neural networks

Author

Feng, Gang ; Palaniswami, M. ; Han, Z.X. ; Chak, C.K.

Author_Institution

Sch. of Electr. Eng., New South Wales Univ., Sydney, NSW, Australia

Volume

5

fYear

1995

fDate

Nov/Dec 1995

Firstpage

2391

Abstract

This paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well known computed torque method and a compensating controller. The compensating controller is realized by using an switch-type structure and an RBF neural network. It is shown that stability of the closed loop system and better tracking performance can be established based on Lyapunov theory. Simulation results are also provided to support our analysis

Keywords

Lyapunov methods; closed loop systems; compensation; feedforward neural nets; neurocontrollers; robot dynamics; robust control; torque control; tracking; Lyapunov theory; closed loop system; compensating controller; computed torque method; joint space; neural networks; radial basis function network; stability; stable robot control; switch-type structure; tracking control; Adaptive control; Artificial neural networks; Control systems; Equations; Manipulator dynamics; Neural networks; Performance gain; Robot control; Robot kinematics; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.487736

Filename

487736