Experimental Validation of an Adaptive Controller for a Mecanum-Wheel Robot with Unknown Center-of-Gravity Offset and Slope Inclination

High-precision path tracking for a Four-Mecanum-Wheel Mobile Robot (FMWMR) is challenged by real-world factors such as payload-induced shifts in the center-of-gravity (CoG) and operation on inclined surfaces. These uncertainties introduce complex, coupled dynamic forces that degrade the performance of conventional controllers. This study addresses this problem with a Model Reference Adaptive Controller (MRAC), which learns and compensates for these unpredictable dynamic effects in real time. To ensure effective operation on physical hardware, the controller incorporates practical solutions for motor friction and control signal stability. The proposed approach is validated through implementation of the MRAC on a Rosmaster X3 robot. A performance comparison is made against a well-tuned Proportional-Integral-Derivative (PID) controller is conducted across twelve distinct scenarios. The results show that the adaptive controller reduced the position Root Mean Square Error (RMSE) by an average of 52.7% and the Integral of Time-weighted Absolute Error (ITAE) by 61.5%. This work validates the MRAC as a powerful and robust solution for robots operating in unpredictable environments.