Adaptive Neural Compliant Force-Position Control of Serial PAM Robot

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dyn...

Full description

Saved in:
Bibliographic Details
Published inJournal of intelligent & robotic systems Vol. 89; no. 3-4; pp. 351 - 369
Main Authors Huy Anh, Ho Pham, Son, Nguyen Ngoc, Van Kien, Cao
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.03.2018
Springer
Springer Nature B.V
Subjects
Adaptive control
Algorithms
Analysis
Artificial Intelligence
Artificial muscles
Contact force
Control
Control algorithms
Control theory
Controllers
Elbow (anatomy)
Electrical Engineering
Engineering
Measuring instruments
Mechanical Engineering
Mechatronics
Muscles
Neural networks
Proportional integral derivative
Rehabilitation
Rehabilitation robots
Robot control
Robot learning
Robotics
Robots
Wrist
Serial PAM robot
Compliant force-position control
End-effecter contact force variations
Nonlinear auto-regressive exogenous (NARX) model
Pneumatic artificial muscle (PAM)
Hybrid adaptive neural ADNN-PID controller
PAM-based rehabilitation robot
Online AccessGet full text

Cover

More Information
Summary:This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-017-0570-1