Paper Info
Title
Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties.
Abstract
Redundant design can greatly improve the flexibility of robot manipulators, but may suffer from potential limitations such as system complicity, model uncertainties, physical limitations, which make it challenging to achieve accurate tracking. In this paper, we propose a novel kinematic controller based on a recurrent neural network(RNN) which is competent in model adaption. An identifier which is related to joint velocity and tracking error is designed to learn the kinematic parameters online. In the inner loop, the redundancy resolution is formulated as a quadratic optimization problem, and a RNN is built to obtain the optimal solution recurrently, and the minimum norm of joint velocity is derived as the secondary task. Theoretical analysis demonstrates the global convergence of tracking error. Compared with existing methods, uncertain kinematic model of the robot is allowed in this paper, and pseudo-inverse of Jacobian matrix is avoided, with the consideration of physical limitations in a joint framework. Numerical and actual experiments based on a serial robot Kinova JACO2 show the effectiveness of the proposed controller.
Year
DOI
Venue
2019
10.1016/j.neucom.2018.11.001
Neurocomputing
Keywords
Field
DocType
Kinematic control,Redundancy resolution,Uncertain kinematics,Dynamic neural network
Inner loop,Control theory,Kinematics,Control theory,Recurrent neural network,Redundancy (engineering),Artificial intelligence,Quadratic programming,Artificial neural network,Machine learning,Mathematics,Tracking error
Journal
Volume
ISSN
Citations 
329
0925-2312
1
PageRank 
References 
Authors
0.35
26
6
Name
Order
Citations
PageRank
Zhihao Xu1189.96
Shuai Li2153.23
Xuefeng Zhou33712.04
Wu Yan421.72
Taobo Cheng522.74
Dan Huang6559.44