Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Abstract

Considering the necessity of merging the physical constraints in joint space for redundant robot motion control in practice by regarding the kinematics of robots, a discrete perturbation-immunity neural network (DPINN) model with high robustness and predominant convergence is proposed in this article for managing the problem. It is worth emphasizing that this proposed neural network is developed to provide a solution algorithm to dispose of the practical applications in robot motion control that is investigated by reforming it into perturbed dynamic quadratic programming (QP) with equality and inequality constraints, which remedies the lack and drawbacks of existing methodologies. Moreover, theoretical verifications and results verify the convergence performance and noise suppression ability of the proposed neural network, which is further verified by simulation examples. In addition, the simulations on robot motion control embody its superiority and powerful versatility compared to the existing methods. In summary, the proposed neural network extends the sphere of application in perturbed dynamic QP with double-bound constraints and offers a feasible scheme capable of controlling robots in a neural-network thought. INDEX TERMS Discrete perturbation-immunity neural network (DPINN), physical constraints, robots, theoretical verifications.

Recurrent Neural Network for State Adjustment of Redundant Manipulators

“…State adjustment refers to the process of moving the manipulator from the current state to the target state [26] without the displacement of the end-effector. Most of the research on the redundant manipulator focuses on motion planning such as the manipulability optimization method stated in [27] during the task execution of the redundant manipulator, but there are few achievements on the problem of state adjustment [28]- [30]. In fact, state adjustment is imperative in certain situations [31], [32].…”