<p>The stable control of wheeled mobile robot (WMR) is crucial for accurate trajectory tracking. However, uncertainty can lead to a decline in WMR tracking performance. The objective of this study is to enhance the trajectory tracking control performance of WMR in uncertain conditions. To achieve this, we present a neural network-based dual closed-loop control system that manages WMR and improves their trajectory tracking capabilities. Firstly, a kinematic model is developed for the WMR to generate virtual velocity based on an adaptive controller in the outer loop. In the inner loop, a dynamic model is established, and the uncertainty is approximated using a neural network. Based on the approximated value from the neural network, an inner loop controller is designed to achieve successful trajectory tracking. Finally, to improve tracking performance, a non-dominated sorting genetic algorithm-II (NSGA-II) algorithm is employed to optimize design parameters. System stability is analyzed using the Lyapunov theory, and the effectiveness of the proposed control scheme is verified by comparing it with different control methods. Compared to previous control studies for WMR, this research presents the following novelties and scientific contributions: 1) In the kinematic controller, an adaptive parameter estimator is integrated into the feedback controller to estimate the unknown parameter. 2) In dynamic controller, a neural network-based control scheme is designed to approximate the lumped disturbances including the unknown parameters and external disturbances. 3) By integrating the NSGA-II with the WMR system, a parametric tuning scheme based on NSGA-II is proposed for optimizing the controller parameters.</p>