Open AccessDesign and experimentation of acceleration‐level drift‐free scheme aided by two recurrent neural networks
Author(s) -
Zhang Zhijun,
Zhang Yug
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2011.0573
Subject(s) - recurrent neural network , acceleration , solver , path (computing) , control theory (sociology) , computer science , artificial neural network , gradient descent , trajectory , robotic arm , algorithm , mathematics , artificial intelligence , mathematical optimization , control (management) , physics , classical mechanics , programming language , astronomy
To solve the joint‐angle and joint‐velocity drift problems in cyclic motion of redundant robot manipulators, an acceleration‐level drift‐free (ALDF) scheme subject to a linear equality constraint is proposed, of which the effectiveness is analysed and proved via the theory of second‐order system. The scheme is then reformulated into a quadratic program (QP). Furthermore, two recurrent neural networks (RNNs) are developed for solving the resultant QP problem. The first RNN solver is based on Zhang et al 's neural‐dynamic method and called Zhang neural network (ZNN), whereas the other is based on the gradient‐descent method and called gradient neural network (GNN). Comparison results based on computer simulations between the ZNN and GNN solvers with a circular‐path tracking task demonstrate that the ZNN solver has faster convergence and fewer errors. In addition, the hardware experiments of tracking a straight‐line path and a rhombic path based on a six degrees of freedom manipulator validate the physical realisability and efficacy of the proposed ALDF scheme and the two RNN QP‐solvers. Moreover, the position, velocity and acceleration error analyses indicate the accuracy of the proposed ALDF scheme and the corresponding RNN QP‐solvers.