Comparative Analysis of Machine Learning Regression Models for Unknown Dynamics

Abstract

System identification methods can enable scientists and engineers to model a system for analysis, estimation, or activity planning. Machine Learning (ML) regression algorithms are a useful data-driven system identification tool that can be used in many fields, such as signal identification, wireless communication, or dynamic modelling. However, researchers must select an appropriate algorithm depending on the system’s complexity. In this study, we evaluate the performance of three ML algorithms: Polynomial Fit, Artificial Neural Network (ANN), and Sparse Identification of Non-linear Dynamics (SINDy), to perform model identification in four different time-invariant dynamic environments. We trained each algorithm using 100 simulated data sets and validated them with ten different trajectories. We compare the results using an error distribution framework, demonstrating that ANN had the lowest prediction error, SINDy had comparable performance for three dynamic environments, but none of the algorithms reliably predicted the discontinuous accelerations. This study demonstrated that spacecraft control systems with continuous dynamics may benefit from ML methods.