双轮腿机器人力位耦合建模与模型预测控制方法
Force-Position Coupling Modeling and Model Predictive Control Method for Bipedal Wheel-Legged Robots

双轮腿机器人融合了轮式平台的高速机动性与腿足系统的地形适应能力，在复杂环境中具备良好的运动性能。然而，其动态稳定性易受外部扰动影响，控制难度较高。为此，本文提出一种面向双轮腿机器人运动控制的自适应力位耦合模型预测控制方法。该方法基于躯干与轮腿系统的动力学建模，在躯干姿态控制中引入模型预测控制(MPC)，实现姿态与主动力的优化分配；随后通过阻抗控制策略，将最优主动力转化为关节力矩，实现轮腿系统的精确控制与动态调节。在TITA平台上的仿真实验表明，该方法实现了在复杂地形与扰动条件下系统的稳定性与鲁棒性。
The bipedal wheel-legged robot integrates the high-speed mobility of wheeled platforms with the terrain adaptability of legged systems, exhibiting excellent motion performance in complex environments. However, its dynamic stability is highly susceptible to external disturbances, posing significant challenges to control. To address this issue, this paper proposes an adaptive force-position coupling model predictive control method for the motion control of bipedal wheel-legged robots. Based on the dynamic modeling of the torso and wheel-legged system, the method incorporates MPC into torso attitude control to achieve optimal distribution of attitude and active forces. Subsequently, an impedance control strategy is employed to convert the optimal active forces into joint torques, enabling precise control and dynamic adjustment of the wheel-legged system. Simulation experiments on the TITA platform demonstrate that the proposed method ensures the system’s stability and robustness under complex terrain and disturbance conditions.