A Varying-Parameter Convergent-Differential Neural Network for Solving Joint-Angular-Drift Problems of Redundant Robot Manipulators

In order to solve the joint-angular-drift problems of redundant robot manipulators, a novel varying-parameter convergent-differential neural network (VP-CDNN) is proposed and exploited. To do so, a quadratic program (QP)-based feedback-considered joint-angulardrift-free (FC-JADF) scheme is first designed and presented. The FC-JADF scheme adopted in this paper is composed of an optimization criterion simultaneously optimizing quadratic and linear terms, and a velocity layer kinematic equation with adding feedback. Second, the FC-JADF scheme is formulated as a standard QP. Third, the VP-CDNN is proposed to solve the resultant standard QP problem. The Lyapunov theory proves that the proposed VP-CDNN solver can globally converge to an optimal solution to the standard QP problem corresponding to redundant robot manipulators, and the joint-angular-drift problems are solved. Two computer simulations and physical experiments based on a six-degree-of-freedom Kinova Jaco
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robot, i.e., a starfish path and a cardioid path, verify the effectiveness, accuracy, safety, and practicability of the QP-based FC-JADF scheme and the VP-CDNN solver for solving the joint-angular-drift problems of redundant robot manipulators.