A variable-gain fixed-time convergent neurodynamic network for time-variant quadratic programming under unknown noises

Biao Song, Tinghe Hong, Weibing Li, Gang Chen, Yongping Pan, Kai Huang

Abstract: This article proposes a variable-gain fixed-time convergent and noise-tolerant error-dynamics based neurodynamic network (VGFxTNT-EDNN) to solve time-varying quadratic programming problems, while being robust to unknown noises. Unlike existing finite-time convergent EDNNs, the newly designed VGFxTNT-EDNN guarantees fixed-time convergence by dynamically adjusting its variable parameters. Moreover, the VGFxTNT-EDNN effectively handles unknown noise, addressing a limitation of existing fixed-time or predefined-time convergent models, which typically assume that the noise is known. Theoretical analysis utilizing Lyapunov theory proves that the VGFxTNT-EDNN possesses fixed-time convergence and robustness properties. Numerical validations demonstrate superior noise tolerance and fixed-time convergence of the VGFxTNT-EDNN, as compared with the existing models. Finally, a path-tracking experiment is conducted by utilizing a Franka Emika Panda robot to verify the practicality of the VGFxTNT-EDNN.