Title
Neuroadaptive Robotic Control Under Time-Varying Asymmetric Motion Constraints: A Feasibility-Condition-Free Approach.
Abstract
This paper presents a neuroadaptive tracking control approach for uncertain robotic manipulators subject to asymmetric yet time-varying full-state constraints without involving feasibility conditions. Existing control algorithms either ignore motion constraints or impose additional feasibility conditions. In this paper, by integrating a nonlinear state-dependent transformation into each step of backstepping design, we develop a control scheme that not only directly accommodates asymmetric yet time-varying motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers, simplifying design process, and making implementation less demanding. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness and benefits of the proposed control method for robotic manipulator are validated via computer simulation.
Year
DOI
Venue
2020
10.1109/TCYB.2018.2856747
IEEE transactions on cybernetics
Keywords
Field
DocType
Manipulators,Artificial neural networks,Time-varying systems,Nonlinear systems,Robot kinematics,Symmetric matrices
Backstepping,Mathematical optimization,Nonlinear system,Control theory,Robot kinematics,Feasibility condition,Precondition,Symmetric matrix,Artificial neural network,Design process,Mathematics
Journal
Volume
Issue
ISSN
50
1
2168-2275
Citations 
PageRank 
References 
15
0.53
21
Authors
2
Name
Order
Citations
PageRank
Kai Zhao110413.74
Yong-Duan Song21949108.61