This work introduces a formulation of model predictive control (MPC) which adaptively reasons about the complexity of the model based on the task while maintaining feasibility and stability guarantees. Existing MPC implementations often handle computational complexity by shortening prediction horizons or simplifying models, both of which can result in instability. Inspired by related approaches in behavioral economics, motion planning, and biomechanics, our method solves MPC problems with a simple model for dynamics and constraints over regions of the horizon where such a model is feasible and a complex model where it is not. The approach leverages an interleaving of planning and execution to iteratively identify these regions, which can be safely simplified if they satisfy an exact template/anchor relationship. We show that this method does not compromise the stability and feasibility properties of the system, and measure performance in simulation experiments on a quadrupedal robot executing agile behaviors over terrains of interest. We find that this adaptive method enables more agile motion and expands the range of executable tasks compared to fixed-complexity implementations.

翻译：本文提出了一种模型预测控制（MPC）的表述形式，该形式能根据任务自适应地推理模型的复杂度，同时保持可行性及稳定性保证。现有的MPC实现通常通过缩短预测时域或简化模型来处理计算复杂度，但这两种方式均可能导致系统失稳。受行为经济学、运动规划和生物力学中相关方法的启发，我们的方法在预测时域内对可行区域采用简单的动力学与约束模型进行求解，而在不可行区域则采用复杂模型。该方法利用规划与执行的交错进行，迭代地识别这些区域——若它们满足精确的模板/锚点关系，则可安全地进行简化。我们证明该方法不会损害系统的稳定性与可行性，并在四足机器人执行敏捷行为穿越目标地形的仿真实验中评估了其性能。研究发现，与固定复杂度的实现方式相比，这种自适应方法能够实现更敏捷的运动，并扩展了可执行任务的范围。