Koopman作用素理論によるソフトロボット動力学の非線形システム同定
Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory
Koopman作用素理論に基づくシステム同定手法をソフトロボットアームに適用し、ニューラルネットやNLARXなど従来モデルより高精度に実機の挙動を予測できることを示した論文。
著者: Daniel Bruder, C. David Remy, Ram Vasudevan
分類: cs.RO
原文アブストラクト
Soft robots are challenging to model due in large part to the nonlinear properties of soft materials. Fortunately, this softness makes it possible to safely observe their behavior under random control inputs, making them amenable to large-scale data collection and system identification. This paper implements and evaluates a system identification method based on Koopman operator theory in which models of nonlinear dynamical systems are constructed via linear regression of observed data by exploiting the fact that every nonlinear system has a linear representation in the infinite-dimensional space of real-valued functions called observables. The approach does not suffer from some of the shortcomings of other nonlinear system identification methods, which typically require the manual tuning of training parameters and have limited convergence guarantees. A dynamic model of a pneumatic soft robot arm is constructed via this method, and used to predict the behavior of the real system. The total normalized-root-mean-square error (NRMSE) of its predictions is lower than that of several other identified models including a neural network, NLARX, nonlinear Hammerstein-Wiener, and linear state space model.
