Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking
In this paper, finite-time Zhang neural networks (ZNNs) are designed to solve time-varying quadratic program (QP) problems and applied to robot tracking. Firstly, finite-time criteria and upper bounds of the convergent time are reviewed. Secondly, finite-time ZNNs with two tunable activation functions are proposed and applied to solve the time-varying QP problems. Finite-time convergent theorems of the proposed neural networks are presented and proved. The upper bounds of the convergent time are estimated less conservatively. The proposed neural networks also have superior robustness performance against perturbation with large implementation errors. Thirdly, feasibility and superiority of our method are shown by numerical simulations. At last, the proposed neural networks are applied to robot tracking. Simulation results also show the effectiveness of the proposed methods.
robot tracking finite-time znn tunable activation function time-varying qp problems upper bound of convergent time