用双层优化实时调整机器人步态，提升稳定性和抗干扰能力
Bilevel Optimization for Real-Time Control with Application to Locomotion Gait Generation
- 高层优化实时调整底层模型预测控制的接触时序参数
- 仿真中实现更好抗扰性与新形态步态，收敛性有理论保证
- 适合需实时动态调整的复杂机器人控制系统研究者
模型预测控制（MPC）是控制非线性现实系统（如四足机器人）的常用方法。然而，快速求解MPC以满足实时需求往往具有挑战性。一种常见解决方案是实时迭代法，不追求完全收敛，而是获得足够接近的近似解。本文将此思想扩展至双层控制框架：高层优化程序调整底层MPC控制器的参数，以生成控制输入和期望状态轨迹。我们提出一种算法，在实时条件下迭代求解该双层问题，并给出收敛性及稳定性改进的条件。通过在四足机器人上的应用验证了该算法的有效性，其中高层问题实时优化接触时序。仿真结果表明，该方法在扰动抑制和最优性方面均有提升，同时生成了定性上全新的步态。
原文摘要 · Abstract (English)
Model Predictive Control (MPC) is a common tool for the control of nonlinear, real-world systems, such as legged robots. However, solving MPC quickly enough to enable its use in real-time is often challenging. One common solution is given by real-time iterations, which does not solve the MPC problem to convergence, but rather close enough to give an approximate solution. In this paper, we extend this idea to a bilevel control framework where a "high-level" optimization program modifies a controller parameter of a "low-level" MPC problem which generates the control inputs and desired state trajectory. We propose an algorithm to iterate on this bilevel program in real-time and provide conditions for its convergence and improvements in stability. We then demonstrate the efficacy of this algorithm by applying it to a quadrupedal robot where the high-level problem optimizes a contact schedule in real-time. We show through simulation that the algorithm can yield improvements in disturbance rejection and optimality, while creating qualitatively new gaits.
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