| Summary: | Two different-level schemes are researched in this article for achieving the kinematic control of redundant manipulators, one of which is exploited at the acceleration level, and the other is at the jerk level. Firstly, they are both reconstructed as a standard quadratic programming problem with different parameter definitions and addressed by a gradient neural network (GNN) method. Secondly, from the perspective of the GNN algorithm, a theoretical interpretation of the intrinsic equivalence between the acceleration-level scheme and jerk-level scheme is performed. Further, simulations on the manipulator synthesized by the two schemes aided with the GNN method tracking two different trajectories are conducted. Finally, comparisons of relevant joint data (i.e., joint angles, joint velocities, and joint accelerations) are presented to substantiate the equivalence between the two schemes, and simulative experiments are carried out at the same time.
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