A Novel Discretized ZNN Model for Velocity Layer Weighted Multicriteria Optimization of Robotic Manipulators With Multiple Constraints

To effectively diminish the kinetic energy dissipation, joint-angle drift, and joint-velocity discontinuity problems, and simultaneously achieve the end-effector position and direction control as well as the avoidance of joint-physical limits, a novel velocity layer weighted multicriteria optimization scheme is proposed, which outperforms the traditional schemes for the robotic manipulators with multiple constraints. Besides, considering that the existing joint-limit conversion strategies are not differentiable everywhere or with relatively complex formulation, a new exponential joint-limit conversion strategy is introduced to facilitate the dynamic quadratic programming reformulation of the proposed scheme. For easier numerical realization and real-time control, aided with a high-precision six-step extrapolated-backward discretization rule, a novel discretized zeroing neural network model is proposed to resolve the proposed scheme, which has higher precision than the existing neural network models. Finally, numerical and physical experiments are conducted to substantiate the efficacy, superiority, and practicability of the proposed scheme and model.