Coupled mass-spring-damper system for nonlinear system identification - actuated with random static inputs - synthetically generated

Version 1.0
Frank, Daniel, 2025, "Coupled mass-spring-damper system for nonlinear system identification - actuated with random static inputs - synthetically generated", https://doi.org/10.18419/DARUS-4768, DaRUS, V1
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Description

Overview

This dataset contains input-output data of a coupled mass-spring-damper system with a nonlinear force profile. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system coupled-msd. The configuration .json files for the corresponding datasets (in-distribution and out-of-distribution) can be found in the respective folders. After creating the dataset, the files are stored in the raw folder. Then, they are split into subsets for training, testing, and validation and can be found in the processed folder; details about the splitting are found in the config.json file.

The dataset can be used to test system identification algorithms and methods that aim to identify nonlinear dynamics from input-output measurements. The training dataset is used to optimize the model parameters, the validation set for hyperparameter optimization, and the test set only for the final evaluation.

In [2], the authors use the same underlying dynamics to create their dataset.

Input generation

Input trajectories are piecewise constant trajectories.

Noise

Gaussian white noise of approximately 30dB is added at the output.

Statistics

The input and output size is one.

  • In-distribution data: 1,500,000 data points
    • Training: 120 trajectories of length 7500
    • Validation: 20 trajectories of length 7500
    • Test: 60 trajectories of length 7500
  • Out-of-distribution data: 10 times 3000 data points

    10 different datasets were only used for testing. Each dataset contains 50 trajectories of length 6000.

References

  1. Frank, D. statesim [Computer software]. https://github.com/Dany-L/statesim
  2. Revay, M., Wang, R., & Manchester, I. R. (2020). A convex parameterization of robust recurrent neural networks. IEEE Control Systems Letters, 5(4), 1363-1368.
Subject Computer and Information Science; Engineering
Keyword Nonlinear System Identification, Dynamical System
Related Publication Cites: Max Revay, Ruigang Wang, Ian R. Manchester: A Convex Parameterization of Robust Recurrent Neural Networks. IEEE Control. Syst. Lett. 5(4): 1363-1368 (2021)doi: 10.1109/LCSYS.2020.3038221
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CC BY 4.0

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Coupled mass-spring-damper system.zip
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Published Feb 26, 2025
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MD5: 04949dfadc2bd01a404223ca93e7d789
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