モデル予測制御のための無限ホライズン価値関数近似
Infinite-Horizon Value Function Approximation for Model Predictive Control
制約付き最適制御問題の無限ホライズン価値関数をニューラルネットワークで近似し、モデル予測制御の終端コストとして用いることで大域的な安定性を実現する手法を提案。
著者: Armand Jordana, Sébastien Kleff, Arthur Haffemayer, Joaquim Ortiz-Haro, Justin Carpentier, Nicolas Mansard, Ludovic Righetti
分類: cs.RO
原文アブストラクト
Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large preview horizons, which are necessary to ensure safety and stability. In practice, practitioners have to carefully design cost functions that can imitate an infinite horizon formulation, which is tedious and often results in local minima. In this work, we study how to approximate the infinite horizon value function of constrained optimal control problems with neural networks using value iteration and trajectory optimization. Furthermore, we experimentally demonstrate how using this value function approximation as a terminal cost provides global stability to the model predictive controller. The approach is validated on two toy problems and a real-world scenario with online obstacle avoidance on an industrial manipulator where the value function is conditioned to the goal and obstacle.
