Fixed-time convergence ZNN model for solving rectangular dynamic full-rank matrices inversion

Abstract

The Moore–Penrose inverse of dynamic matrices has found widespread application and has garnered significant attention. The zeroing neural network (ZNN) has proven to be an effective solution for computing the Moore–Penrose inverse in dynamic matrices. This paper proposes a novel unified fixed-time ZNN (UFTZNN) model designed to achieve fixed-time convergence and solve both left and right inverse problems using a single model. Theoretical analysis of the convergence and robustness of the UFTZNN model is rigorously presented. Numerical simulations comparing the UFTZNN with existing ZNN models confirm its superiority in addressing left and right inverse problems, convergence time, and robustness. The UFTZNN model is applied to the inverse kinematic tracking problem of a six-degree-of-freedom manipulator-based photoelectric tracking system to demonstrate its potential applications and effectiveness.