A Sensor-Enhanced Hierarchical Torque Vector Control for Distributed Mobile Robot on Slippery Road

Hierarchical torque vector control has proven effective in enhancing stability and handling in vehicles; however, its application in mobile robots faces challenges due to dynamic model variations and hardware differences. In response, this paper introduces a novel framework that leverages rich sensor information to optimize torque vector control in mobile robots. In this paper, we establish a comprehensive total torque parameter-varying error (TTPE) model at the upper level, effectively decoupling the yaw moment generated by the steering angle from the additional yaw moment. To achieve accurate and stable control, we employ a TTPE model predictive controller (TTPE-MPC) that generates precise desired total torque. Furthermore, we introduce a slip loss objective function based on kinematics in the middle layer, in addition to the conventional torque error and tire load objective functions. We enhance the optimization of tire load by incorporating information on the six-dimensional leg force, which significantly improves maneuverability and tracking precision during the torque distribution stage. To further enhance precision and real-time performance of torque distribution, a disturbance observer is designed for each individual wheel to estimate the tire's longitudinal force at the lower layer. The control law of the motor input current is then designed based on this estimation, resulting in improved control accuracy. The proposed framework is validated through experiments conducted on a six-wheeled robot. The results demonstrate that the stability of the expected value of the total moment improves, with tracking errors of velocity and angular velocity reduced by approximately 43%. Moreover, the slip loss between wheels is reduced by approximately 46% compared to conventional methods. Note to Practitioners —This article aims to improve robotic steering maneuverability and provide safer, stable solutions for autonomous vehicles with independent propulsion. Current methods for robot operation often lack optimal torque management, primarily focusing on direct speed control while neglecting valuable sensor data. We propose a model-based, hierarchical control strategy to efficiently allocate torque to the robot's propulsion motors. Our approach establishes a model that distinguishes between angular difference and motor torque, employing a Total Torque Parameter-Varying Error Model Predictive Controller (TTPE-MPC) for cumulative torque calculation. Optimization metrics, with a focus on sensor data, guide torque allocation, taking anti-slip considerations into account. Furthermore, a speed-based control observer ensures fair torque distribution, theoretically converging expected, estimated, and true values. Initial robot experiments validate reduced speed and angular velocity errors, supporting the effectiveness of our approach. Future work will explore incorporating suspension system aerodynamics into torque distribution, further enhancing stability.