Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment

Abstract Based on a new robust zeroing neural network (RZNN) model, the trajectory tracking control of a wheeled mobile robot (WMR) within fixed-time in noise-polluted environment is presented in this paper. Unlike most of the previous reported works, the RZNN model approach for trajectory tracking control of the WMR reaches fixed-time convergence and noise suppression simultaneously. Besides, detailed theoretical analysis of its convergence and robustness are provided. Numerical simulation verification is also provided to demonstrate the superior robustness and accurateness of the RZNN model approach for trajectory tracking control of the WMR in noise-polluted environment. Both of the theoretical analysis and numerical simulation results verify the effectiveness and robustness of the RZNN model approach.