最適ホライズン模型予測制御と微分動的計画法
Optimal-Horizon Model-Predictive Control with Differential Dynamic Programming
微分動的計画法に基づき、予測ホライズンをオンラインで決定する軌道最適化アルゴリズムを提案し、障害物回避問題で標準MPCより有効であることを示した。
著者: Kyle Stachowicz, Evangelos A. Theodorou
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
We present an algorithm, based on the Differential Dynamic Programming framework, to handle trajectory optimization problems in which the horizon is determined online rather than fixed a priori. This algorithm exhibits exact one-step convergence for linear, quadratic, time-invariant problems and is fast enough for real-time nonlinear model-predictive control. We show derivations for the nonlinear algorithm in the discrete-time case, and apply this algorithm to a variety of nonlinear problems. Finally, we show the efficacy of the optimal-horizon model-predictive control scheme compared to a standard MPC controller, on an obstacle-avoidance problem with planar robots.
