The feasibility region of a Model Predictive Control (MPC) algorithm is the subset of the state space in which the constrained optimization problem to be solved is feasible. In our recent Intrinsically Stable MPC (IS-MPC) method for humanoid gait generation, feasibility means being able to satisfy the dynamic balance condition, the kinematic constraints on footsteps as well as an explicit stability condition. Here, we exploit the feasibility concept to build a step timing adapter that, at each control cycle, modifies the duration of the current step whenever a feasibility loss is imminent due, e.g., to an external perturbation. The proposed approach allows the IS-MPC algorithm to maintain its linearity and adds a negligible computational burden to the overall scheme. Simulations and experimental results where the robot is pushed while walking showcase the performance of the proposed approach.

中文翻译：

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适用于类人动物中基于MPC的稳健步态生成的可行性驱动步长时序自适应

模型预测控制（MPC）算法的可行性区域是状态空间的子集，在该状态空间中要解决的约束优化问题是可行的。在我们最近的用于类人步态生成的本质稳定MPC（IS-MPC）方法中，可行性手段能够满足动态平衡条件，脚步运动学约束以及明确的稳定性条件。在这里，我们利用可行性概念来构建步进定时适配器，该适配器在每个控制周期都会在由于外部干扰而导致可行性损失迫在眉睫时修改当前步骤的持续时间。所提出的方法允许IS-MPC算法保持其线性，并为整个方案增加可忽略的计算负担。