複雑な非線形動的システムのロバスト制御のための適応的Koopman埋め込み
Adaptive Koopman Embedding for Robust Control of Complex Nonlinear Dynamical Systems
Koopman埋め込みをオンラインで適応的に修正するニューラルネットワークを提案し、モデル予測制御と組み合わせることで、外乱やパラメータ変動に対するロバスト性を向上させた。
著者: Rajpal Singh, Chandan Kumar Sah, Jishnu Keshavan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
The discovery of linear embedding is the key to the synthesis of linear control techniques for nonlinear systems. In recent years, while Koopman operator theory has become a prominent approach for learning these linear embeddings through data-driven methods, these algorithms often exhibit limitations in generalizability beyond the distribution captured by training data and are not robust to changes in the nominal system dynamics induced by intrinsic or environmental factors. To overcome these limitations, this study presents an adaptive Koopman architecture capable of responding to the changes in system dynamics online. The proposed framework initially employs an autoencoder-based neural network that utilizes input-output information from the nominal system to learn the corresponding Koopman embedding offline. Subsequently, we augment this nominal Koopman architecture with a feed-forward neural network that learns to modify the nominal dynamics in response to any deviation between the predicted and observed lifted states, leading to improved generalization and robustness to a wide range of uncertainties and disturbances compared to contemporary methods. Extensive tracking control simulations, which are undertaken by integrating the proposed scheme within a Model Predictive Control framework, are used to highlight its robustness against measurement noise, disturbances, and parametric variations in system dynamics.
