Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory

TL;DR

This paper applies Koopman operator theory to nonlinear system identification of soft robot dynamics, enabling more accurate modeling and prediction without manual parameter tuning.

Contribution

It introduces a Koopman-based method for modeling soft robot dynamics that outperforms traditional approaches in prediction accuracy.

Findings

Lower NRMSE in predictions compared to neural networks and other models

Effective modeling of pneumatic soft robot arm dynamics

No manual tuning required for the Koopman-based approach

Abstract

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.