A stable neural adaptive force controller for a hydraulic actuator

Author

Daachi, B. ; Benallegue, A. ; Sirdi, N. K M

Author_Institution

Lab. de Robotique, Paris, France

Volume

4

fYear

2001

fDate

2001

Firstpage

3465

Abstract

A neural network adaptive force controller is proposed for a real hydraulic system. The dynamic model of this system is highly non-linear and very complex to obtain. Thus, it is considered as a black box, and a priori identification becomes necessary. A neural network is used to approximate the model, then a controller using the Lyapunov approach is designed. The neural network parameters are updated online according to an adaptation algorithm obtained via stability analysis. The performance of the proposed neural network controller is validated on an experimental plant.

Keywords

Lyapunov methods; actuators; adaptive control; control system synthesis; force control; force sensors; hydraulic control equipment; identification; neurocontrollers; position control; stability; three-term control; Lyapunov approach; a priori identification; adaptation algorithm; black box model; dynamic model; hydraulic actuator; neural network controller; stability analysis; stable neural adaptive force controller; Adaptive control; Adaptive systems; Control systems; Force control; Hydraulic actuators; Hydraulic systems; Neural networks; Nonlinear dynamical systems; Programmable control; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on

ISSN

1050-4729

Print_ISBN

0-7803-6576-3

Type

conf

DOI

10.1109/ROBOT.2001.933154

Filename

933154