移动表面上双足运动学的线性倒立摆模型应急模型预测控制
Contingency Model Predictive Control for Bipedal Locomotion on Moving Surfaces with a Linear Inverted Pendulum Model
AI总结：
针对移动表面上双足行走的平衡控制难题，提出基于线性倒立摆模型的应急模型预测控制（CMPC），通过纳入最坏情况表面运动，在仿真中验证了其优于常规MPC的可行性与性能。
摘要：
在动态移动表面（如船舶和车辆）上，腿式机器人行走器的步态控制因平衡控制驱动受限和表面运动未知而具有挑战性。我们提出了一种基于线性倒立摆（LIP）模型的移动表面上双足行走器运动的应急模型预测控制（CMPC）。CMPC是一种稳健设计，建立在常规模型预测控制（MPC）之上，以纳入移动表面的“最坏情况”预测运动。与LIP模型和行走稳定性约束集成，CMPC框架生成一组考虑表面运动预期不确定性的一致控制输入。进行了仿真结果以及与常规MPC在双足行走上的比较，并予以呈现。结果证实了所提出的CMPC设计在各种移动表面运动轮廓下相对于常规MPC的可行性和优越性能。
英文摘要：
Gait control of legged robotic walkers on dynamically moving surfaces (e.g., ships and vehicles) is challenging due to the limited balance control actuation and unknown surface motion. We present a contingent model predictive control (CMPC) for bipedal walker locomotion on moving surfaces with a linear inverted pendulum (LIP) model. The CMPC is a robust design that is built on regular model predictive control (MPC) to incorporate the "worst case" predictive motion of the moving surface. Integrated with an LIP model and walking stability constraints, the CMPC framework generates a set of consistent control inputs considering to anticipated uncertainties of the surface motions. Simulation results and comparison with the regular MPC for bipedal walking are conducted and presented. The results confirm the feasibility and superior performance of the proposed CMPC design over the regular MPC under various motion profiles of moving surfaces.
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