Path Tracking Control for a Transformable Wheel-Legged Robot Using Model Predictive Control

Abstract

Transformable wheel-legged robots can adjust their configuration according to terrain conditions, enabling effective operation in harsh environments. While existing controllers based on preset commands have successfully demonstrated the feasibility of reconfigurable mechanisms, they still struggle to handle complex autonomous operations. To address this, we develop a comprehensive motion model for such robots, encompassing chassis kinematics, chassis-wheel kinematics, and stability models, along with a hierarchical path tracking method. The upper controller uses model predictive control with an error state-space model to optimize real-time tracking error under input constraints and generate desired commands. The lower controller utilizes feedforward control to convert desired inputs into actual ones, while accommodating physical constraints and geometric coupling associated with variable-radius wheels. Comparative analyses confirm the effectiveness of the proposed approach and demonstrate the robot's performance under different wheel modes.