周期軌道追従を完璧に：周期オブザーバ付きモデル予測制御（Π-MPC）
Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer ($\Pi$-MPC)
周期外乱を推定・補償するオブザーバを組み込むことで、モデル誤差があっても周期軌道を漸近的に完全追従できるMPC手法を提案し、ソフトロボットと自動運転カーの実験で有効性を示した。
著者: Luis Pabon, Johannes Köhler, John Irvin Alora, Patrick Benito Eberhard, Andrea Carron, Melanie N. Zeilinger, Marco Pavone
分類: cs.RO, cs.SY, eess.SY, math.OC
原文アブストラクト
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many trajectories of interest are periodic, we show that perfect tracking is possible when incorporating a simple observer that estimates and compensates for periodic disturbances. We present the design of the observer and the accompanying tracking MPC scheme, proving that their combination achieves zero tracking error asymptotically, regardless of the complexity of the unmodelled dynamics. We validate the effectiveness of our method, demonstrating asymptotically perfect tracking on a high-dimensional soft robot with nearly 10,000 states and a fivefold reduction in tracking errors compared to a baseline MPC on small-scale autonomous race car experiments.
