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.

翻译：在动态移动表面（如船舶和车辆）上的足式机器人步态控制因平衡控制驱动受限及未知表面运动而极具挑战性。本文提出一种基于线性倒立摆（LIP）模型的应急模型预测控制（CMPC）方法，用于双足行走机器人在移动表面上的运动控制。CMPC是一种鲁棒性设计，其在常规模型预测控制（MPC）框架下集成移动表面的“最坏情况”预测运动。通过结合LIP模型与行走稳定性约束，CMPC框架能够生成一组考虑表面运动预期不确定性的协调控制输入。本文开展了双足行走仿真实验，并与常规MPC方法进行对比分析。结果表明，在不同移动表面运动轮廓下，所提出的CMPC设计相比常规MPC具有可行性与更优性能。