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:这项工作引入了模型预测控制(MPC)的配方,该配方在保持可行性和稳定性保障的同时,对基于任务的模式的复杂性有适应性的理由。现有的多氯三联苯实施通常通过缩短预测前景或简化模式来处理计算复杂性,这两种模式都可能导致不稳定。受行为经济学、运动规划和生物机械学相关方法的启发,我们的方法解决了多氯三联苯问题,采用了一个简单的模式,用于在这种模式可行和不可行、复杂模式不可行的情况下对地平线进行动态和制约。该方法利用规划和执行的插接来迭接来识别这些地区,如果它们符合精确的模板/锚关系,这些区域可以安全地简化。我们表明,这种方法不会损害系统的稳定性和可行性,并且测量四重机器人在兴趣地形上执行灵活动作的模拟实验的性能。我们发现,这种适应方法能够更灵活地进行运动,扩大可执行的任务的范围,与固定的兼容性执行相比。