二足歩行のための確率的・ロバストMPC：ロバスト性と性能に関する比較研究
Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance
二足歩行の不確実性に対処するため線形確率MPC（SMPC）を初めて適用し、制約違反確率を許容しつつ性能を保てることを示し、ロバストMPCと比較した。
著者: Ahmad Gazar, Majid Khadiv, Andrea Del Prete, Ludovic Righetti
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Linear Model Predictive Control (MPC) has been successfully used for generating feasible walking motions for humanoid robots. However, the effect of uncertainties on constraints satisfaction has only been studied using Robust MPC (RMPC) approaches, which account for the worst-case realization of bounded disturbances at each time instant. In this letter, we propose for the first time to use linear stochastic MPC (SMPC) to account for uncertainties in bipedal walking. We show that SMPC offers more flexibility to the user (or a high level decision maker) by tolerating small (user-defined) probabilities of constraint violation. Therefore, SMPC can be tuned to achieve a constraint satisfaction probability that is arbitrarily close to 100\%, but without sacrificing performance as much as tube-based RMPC. We compare SMPC against RMPC in terms of robustness (constraint satisfaction) and performance (optimality). Our results highlight the benefits of SMPC and its interest for the robotics community as a powerful mathematical tool for dealing with uncertainties.
