A zeroing feedback gradient-based neural dynamics model for solving dynamic quadratic programming problems with linear equation constraints in finite time

Gradient-based neural dynamics (GND) models are a classical algorithm for solving optimization problems, but it has non-negligible flaws in solving dynamic problems. In this study, a novel GND model, namely the zeroing feedback gradient-based neural dynamics (ZF-GND) models, is proposed based on the original GND model for tracking down the exact solution of dynamic quadratic programming problem (DQP). Further, a nonlinear projection function is designed to accelerate the convergence of the model. An upper bound on the convergence time of the ZF-GND model is rigorously defined through theoretical analysis. The superior effect of the ZF-GND model in terms of convergence is verified through comparison experiments. Finally, an application of robot motion planning is introduced to verify the practicality of the ZF-GND model.