Coordinated Trajectory Tracking and Self-Balancing Control for Unmanned Bicycle Robot Against Disturbances

Trajectory tracking and self-balancing capacity is crucial for an unmanned bicycle robot (UBR) applied in off-road trails and narrow space. However, self-balancing is hard to be guaranteed once the steering angle manipulates for the tracking task, both of which are closely linked to the steering angle, especially for the UBR without auxiliary mechanism. In this paper, we introduce a double closed-loop framework in which the outer loop controller plans the desired speed and heading angle to track the reference trajectory, and the inner loop controller track the desired signals obtained from the outer loop to maintain balance. To be specific, a saturated velocity planner is developed to realize fast convergence of tracking error considering the kinematic constraints in the outer loop. A fuzzy sliding model controller (FSMC) is designed to attenuate the chattering effect via adapting its control gain in the inner loop, and a radial basis function neural network (RBFNN) approximator is also integrated into the framework to enhance the adaptability and robustness against bounded disturbances. The feasibility and effectiveness of the proposed control framework and approaches are validated based on the Matlab and Gazebo environment. In particular, the UBR can follow the testing route with lateral deviation less than 0.5 m in the presence of lateral winds and physical parameter measurement error, and comparative simulation results highlighted the superiority of the proposed control scheme.