Adaptive neural network control of robot manipulators in task space

Author

Ge, Shuzhi S. ; Hang, C.C. ; Woon, L.C.

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

44

Issue

6

fYear

1997

fDate

12/1/1997 12:00:00 AM

Firstpage

746

Lastpage

752

Abstract

In this paper, the adaptive neural network control of robot manipulators in the task space is considered. The controller is developed based on a neural network modeling technique which neither requires the evaluation of inverse dynamical model nor the time-consuming training process. It is shown that, if Gaussian radial basis function networks are used, uniformly stable adaptation is assured and asymptotically tracking is achieved. The controller thus obtained does not require the inverse of the Jacobian matrix. In addition, robust control can be easily incorporated to suppress the neural network modeling errors and the bounded disturbances. Numerical simulations are provided to show the effectiveness of the approach

Keywords

adaptive control; asymptotic stability; control system analysis; control system synthesis; feedforward neural nets; manipulators; neurocontrollers; numerical analysis; robust control; Gaussian radial basis function networks; adaptive neural network control; asymptotically tracking; bounded disturbances suppression; control design; control simulation; modeling errors suppression; neural network modeling technique; numerical simulations; robot manipulators; task space; uniformly stable adaptation; Adaptive control; Adaptive systems; Inverse problems; Jacobian matrices; Manipulator dynamics; Neural networks; Orbital robotics; Programmable control; Radial basis function networks; Robot control;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/41.649934

Filename

649934