Stabilization and Point-to-Point Motion Control for Unicycle Robot

Unicycle robots offer high maneuverability in confined spaces, making them suitable for agile micro-logistics and compact automation environments. However, their inherent instability and strong coupling between motion axes make it challenging to maintain balance and achieve reliable point-to-point movement. These problems become even more challenging in the presence of external disturbances and sensor noise, which can reduce control accuracy and affect stability. In order to address these issues, this study proposes a dual-loop control framework that explicitly models disturbances acting on the input and noise affecting the measurements. The robot features a single driving wheel and two reaction wheels placed in vertical and horizontal orientations, which are used for forward motion, lateral balancing, and turning control. Disturbances from dynamic coupling are treated as random signals at the input, while sensor errors are modeled as white noise in the output. A control strategy based on state observation is employed, where the inner loop stabilizes the pitch axis, and the outer loop guides the robot toward target positions. For lateral control, an LQR method is used for roll stabilization, while pole placement-based state feedback is used for attitude tracking. Simulation results demonstrate that the proposed controller enables the robot to maintain its upright posture and reach designated positions, even in the presence of sensor noise and external disturbances.