2022/12/12 by Daniel Frank, Decky Aspandi Latif, Frank, Daniel +7
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Ship Hydrodynamics and Maneuverability #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.05781
openalex publication_date 2022/12/12 · openalex created_date 2023/01/03 · openalex updated_date 2026/07/28
Recurrent neural networks are capable of learning the dynamics of an unknown nonlinear system purely from input-output measurements. However, the resulting models do not provide any stability guarantees on the input-output mapping. In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances. By introducing constraints on the parameters, we can guarantee finite gain stability and incremental finite gain stability. We apply this identification method to learn the motion of a four-degrees-of-freedom ship that is moving in open water and compare it against other purely learning-based approaches with unconstrained parameters. Our analysis shows that the constrained recurrent neural network has a lower prediction accuracy on the test set, but it achieves comparable results on an out-of-distribution set and respects stability conditions.