Prediction of Nonlinear Oscillations in a Jumping Quarter-Car Model Using Reservoir Computing
Abstract
Reliable prediction of vehicle dynamics is essential for smart driving applications such as autonomous control and advanced driver-assistance systems. Off-road vehicles used in agricultural and construction settings are particularly prone to nonlinear behavior, including bifurcations and chaotic motion arising from intermittent loss of tire--road contact. Predicting such dynamics is challenging because it requires resolving both smooth nonlinearities and the discontinuous switching associated with contact loss. In this work, we investigate the feasibility of reservoir computing (RC) -- specifically an echo state network (ESN) -- for data-driven prediction of a jumping quarter-car model. The reservoir is trained on time-series data from a small number of points and evaluated on its ability to reconstruct bifurcation diagrams, phase-space attractors, and time trajectories across periodic and chaotic regimes. The trained reservoir qualitatively reproduces the period-doubling route to chaos, captures the geometric structure of periodic and chaotic attractors. These results demonstrate that reservoir computing is a feasible data-driven predictor of nonlinear dynamics in a practical, non-smooth vehicle system.
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BibTeX
@misc{watanabe2026prediction,
title = {{Prediction of Nonlinear Oscillations in a Jumping Quarter-Car Model Using Reservoir Computing}},
author = {Masahisa Watanabe and Shiva Dixit and Nirmal Punetha and Swati Chauhan and Manish Dev Shirimali},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22205},
eprint = {2609.22205},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22205},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Prediction of Nonlinear Oscillations in a Jumping Quarter-Car Model Using Reservoir Computing
AU - Masahisa Watanabe
AU - Shiva Dixit
AU - Nirmal Punetha
AU - Swati Chauhan
AU - Manish Dev Shirimali
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.22205
UR - https://arxiv.org/abs/2609.22205
AB - Reliable prediction of vehicle dynamics is essential for smart driving applications such as autonomous control and advanced driver-assistance systems. Off-road vehicles used in agricultural and construction settings are particularly prone to nonlinear behavior, including bifurcations and chaotic motion arising from intermittent loss of tire--road contact. Predicting such dynamics is challenging because it requires resolving both smooth nonlinearities and the discontinuous switching associated with contact loss. In this work, we investigate the feasibility of reservoir computing (RC) -- specifically an echo state network (ESN) -- for data-driven prediction of a jumping quarter-car model. The reservoir is trained on time-series data from a small number of points and evaluated on its ability to reconstruct bifurcation diagrams, phase-space attractors, and time trajectories across periodic and chaotic regimes. The trained reservoir qualitatively reproduces the period-doubling route to chaos, captures the geometric structure of periodic and chaotic attractors. These results demonstrate that reservoir computing is a feasible data-driven predictor of nonlinear dynamics in a practical, non-smooth vehicle system.
ER -