Cascaded Tanks with Overflow
The cascaded tanks system is a fluid level control system consisting of two tanks with free outlets fed by a pump. The input signal controls a water pump that delivers the water from a reservoir into the upper water tank. The water of the upper tank flows through a small opening into the lower tank, and finally through a small opening from the lower tank back into the reservoir. This benchmark combines soft and hard (due to overflow) nonlinearities to be identified based on relatively short data records.
A detailed formulation of the identification problem can be found here. All the provided files and information on the cascaded tanks system are available for download here. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures and a video of the setup. The data is available in the .csv and .mat file format.
Previously published results on the Cascaded Tanks benchmark are listed here and below. You can submit your own results through this form. Note that the reported results are curated, only complete submissions with meaningful contributions will be included. Candidate entries should make use of the Python dataloader functionalities and figure of merit calculation functions provided through this link.
Special thanks to Torbjörn Wigren and Per Mattsson for their help in creating this benchmark.
Cite
Please refer to the Cascaded Tanks benchmark as:
M. Schoukens and J.P. Noël, Three Benchmarks Addressing Open Challenges in Nonlinear System Identification, 20th World Congress of the International Federation of Automatic Control, pp.448-453, Toulouse, France, July 9-14, 2017, doi: 10.1016/j.ifacol.2017.08.071. (published version, preprint)
M. Schoukens, P. Mattsson, T. Wigren and J.P. Noël, Cascaded tanks benchmark combining soft and hard nonlinearities, 4TU.ResearchData, Dataset, doi: 10.4121/12960104.
Benchmark Results
You can submit your own results through this form. Note that the reported results are curated, only complete submissions with meaningful contributions will be included. Candidate entries should make use of the Python dataloader functionalities and figure of merit calculation functions provided through this link.