Simulations and experiments of ZNN for online quadratic programming applied to manipulator inverse kinematics

• Published in: International Conference on Information Science and Technology pp. 265 - 270
• Main Authors: Zhang, Yunong, Wang, Ying, Jin, Long, Chen, Junwei, Yang, Yiwen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2013
• Subjects: Computational modeling; Equations; Joints; Manipulators; Mathematical model; Trajectory
• ISBN: 1467351377; 9781467351379
• ISSN: 2164-4357
• DOI: 10.1109/ICIST.2013.6747548

Zhang neural network (ZNN), a special class of recurrent neural network (RNN), has recently been introduced for time-varying convex quadratic-programming (QP) problems solving. In this paper, a drift-free robotic criterion is exploited in the form of a quadratic performance index. This repetitive-motion-planning (RMP) scheme can be reformulated into a time-varying quadratic program subject to a linear-equality constraint. As QP real-time solvers, two recurrent neural networks, i.e., Zhang neural network and gradient neural network (GNN), are then developed for the online solution of the time-varying QP problem. Computer simulations performed on a four-link robot manipulator demonstrate the superiority of the ZNN solver, compared to the GNN one. Moreover, robotic experiments conducted on a six degrees-of-freedom (DOF) motor-driven push-rod (MDPR) redundant robot manipulator substantiate the physical realizability and effectiveness of this RMP scheme using the ZNN solver.