未知ダイナミクスを持つシステムのモデリングと制御における双線形Koopman実現の利点
Advantages of Bilinear Koopman Realizations for the Modeling and Control of Systems with Unknown Dynamics
制御アフィン系は無限次元の双線形Koopman実現を持つが線形実現は必ずしも持たないことを示し、ロボットアームのモデル予測制御で双線形実現が線形より高精度かつ非線形より高効率であることを実証した。
著者: Daniel Bruder, Xun Fu, Ram Vasudevan
分類: cs.RO
原文アブストラクト
Nonlinear dynamical systems can be made easier to control by lifting them into the space of observable functions, where their evolution is described by the linear Koopman operator. This paper describes how the Koopman operator can be used to generate approximate linear, bilinear, and nonlinear model realizations from data, and argues in favor of bilinear realizations for characterizing systems with unknown dynamics. Necessary and sufficient conditions for a dynamical system to have a valid linear or bilinear realization over a given set of observable functions are presented and used to show that every control-affine system admits an infinite-dimensional bilinear realization, but does not necessarily admit a linear one. Therefore, approximate bilinear realizations constructed from generic sets of basis functions tend to improve as the number of basis functions increases, whereas approximate linear realizations may not. To demonstrate the advantages of bilinear Koopman realizations for control, a linear, bilinear, and nonlinear Koopman model realization of a simulated robot arm are constructed from data. In a trajectory following task, the bilinear realization exceeds the prediction accuracy of the linear realization and the computational efficiency of the nonlinear realization when incorporated into a model predictive control framework.
