Paper Info
Title
Adaptive PD networks tracking control with full-state constraints for redundant parallel manipulators.
Abstract
In this paper, a detailed study to apply an adaptive proportional-derivative networks (APDNs) tracking control is proposed for redundant parallel manipulators with full-state constraints. APDNs is designed - based on the combination of the nonlinear compensation, the PD nonlinear sliding part, and feedforward radial basic function neural networks (RBFNs) with online learning. By using the Lyapunov method, the stability of the closed-loop system in the case of full-state constraints is proven. The proposed control strategy has better dynamic performance and higher robustness in comparison with PD-sliding mode control and augmented nonlinear PD control. The effectiveness proposed control is illustrated by simulation.
Year
Venue
Keywords
2017
Joint International Conference on Soft Computing and Intelligent Systems SCIS and International Symposium on Advanced Intelligent Systems ISIS
Parallel manipulator,radial basic function neural networks,adaptive control,robotics trajectory
Field
DocType
ISSN
Lyapunov function,Parallel manipulator,Nonlinear system,Computer science,Control theory,Robustness (computer science),Adaptive control,Mode control,Artificial neural network,Feed forward
Conference
2377-6870
Citations 
PageRank 
References 
1
0.35
8
Authors
5
Name
Order
Citations
PageRank
Van-Truong Nguyen121.12
Chyi-Yeu Lin27114.95
Shun-Feng Su3119497.62
Ngoc-Quan Nguyen410.69
Quoc-Viet Tran510.69