Novel Zeroing Neural Dynamics for Real-Time Management of Multi-Vehicle Cooperation

In multi-agent real-time position management tasks, the accuracy of error convergence and convergence time are crucial. This paper reformulates the proposed real-time position management scheme as a quadratic programming problem with equality constraints and solves it in real-time using the zeroing neural dynamics (ZND) model. To enhance the model's ability to detect real-time position management errors, an adaptive parameter finite-time convergent zeroing neural dynamics (AP-FTZND) model is introduced, incorporating adaptive parameters and a nonlinear activation function (AF) within the ZND framework. The global convergence of the AP-FTZND model is proven using the Lyapunov theory, and the upper bound of the convergence time is derived. Finally, the effectiveness and superiority of the AP-FTZND model in solving multi-agent real-time position management tasks are validated through simulations and physical experiments.