非線形系に対するロバスト低次元モデル予測制御
Robust Nonlinear Reduced-Order Model Predictive Control
高次元非線形システムを低次元モデルで予測制御する際に生じるモデル化誤差を動的に見積もり、制約充足と安定性を保証するロバストMPC手法を提案し、約1万状態のソフトロボットで有効性を示した。
著者: John Irvin Alora, Luis A. Pabon, Johannes Köhler, Mattia Cenedese, Ed Schmerling, Melanie N. Zeilinger, George Haller, Marco Pavone
分類: eess.SY, cs.RO, cs.SY, math.OC
原文アブストラクト
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time. While reduced-order models (ROMs) are frequently employed in model-based control schemes, dimensionality reduction introduces model uncertainty which can potentially compromise the stability and safety of the original high-dimensional system. In this work, we propose a novel reduced-order model predictive control (ROMPC) scheme to solve constrained optimal control problems for nonlinear, high-dimensional systems. To address the challenges of using ROMs in predictive control schemes, we derive an error bounding system that dynamically accounts for model reduction error. Using these bounds, we design a robust MPC scheme that ensures robust constraint satisfaction, recursive feasibility, and asymptotic stability. We demonstrate the effectiveness of our proposed method in simulations on a high-dimensional soft robot with nearly 10,000 states.
