Adaptive Fuzzy Feedback Linearization Control for Trajectory Tracking of Two-Wheeled Mobile Robots

This paper presents an Adaptive Fuzzy Feedback Linearization Control (AFFLC) strategy for trajectory tracking of two-wheeled mobile robots (WMRs) subject to modeling uncertainties and external disturbances. The proposed approach employs feedback linearization to transform the nonlinear robot dynamics into a structured tracking error model, which serves as a systematic basis for controller synthesis. An adaptive fuzzy inference system is subsequently integrated to estimate and compensate for unmodeled nonlinearities and unknown disturbances in real time, thereby enhancing robustness and adaptability. A Lyapunov-based stability analysis is conducted to demonstrate that the closed-loop system satisfies the uniform ultimate boundedness (UUB) property, ensuring bounded tracking errors in the presence of parameter variations. Simulation studies involving figure-eight and elliptical trajectories under 30% modeling uncertainties and external disturbances verify that the proposed AFFLC achieves improved tracking accuracy, smoother heading regulation, and stronger disturbance rejection compared with conventional nonlinear control methods.