Adaptive neural network controller for trajectory tracking of wheel mobile robots with velocity constraints
DOI:
https://doi.org/10.54939/1859-1043.j.mst.110.2026.22-33Keywords:
Mobile robot; Adaptive control; Velocity constraint; Neural network; Backstepping; Trajectory tracking.Abstract
This paper presents an adaptive neural control scheme for trajectory tracking of a nonholonomic wheeled mobile robot (WMR) under velocity constraints. The control objective is to ensure stable motion tracking despite model uncertainties and external disturbances. An adaptive neural network (ANN) is employed to approximate unknown nonlinearities in the robot dynamics, while an auxiliary control law compensates for velocity limitations and ensures safe operation within the admissible region. The proposed design combines model-based feedback with data-driven adaptation to improve tracking accuracy and robustness. The stability of the overall closed-loop system is demonstrated through Lyapunov analysis, guaranteeing the convergence of the tracking errors. Simulation results validate that the proposed ANN-based controller achieves faster convergence, smaller steady-state errors, and stronger robustness compared to conventional model-based controllers.
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