If you don't see your institution, add your dataset to the main dataverse named "RepOD".
Select the dataverse to which you want to add the new dataset:
You need to Sign In/Sign Up to add a dataset.
Share this dataset on your favorite social media networks.
Maniarski, Robert, 2024, "Source data for: Fault-tolerant design of non-linear iterative learning control using neural networks", https://doi.org/10.18150/AU7YHH, RepOD, V1
Learn about Data Citation Standards.
The design of neural-network-based iterative learning control for non-linear systems is addressed in the setting of a fault tolerant control regime. Taking advantage of the repetitive character of the control task, the inherent uncertainty related to a potential faulty system state can be properly accommodated in terms of a data-driven iterative learning scheme with neural networks used for forward/inverse modeling as well as for controller synthesis. The resulting control technique is supposed to be flexible enough to accurately compensate the faults occurring on both the sensors and actuators and, additionally, take into account the disturbances and noise acting on the system. A complete characterization of the novel fault-tolerant iterative learning scheme is provided including system identification, fault detection and accommodation. Also, the painstaking convergence analysis is presented and the resulting sufficient conditions can be constructively used to determine the update of control law in the consecutive process trial. The excellent performance of the developed control scheme is illustrated by a nontrivial example of tracking control for a magnetic brake system on various scenarios involving actuator and/or sensor faults.
Iterative learning control; Convergence; Neural networks; Fault tolerance; Fault accommodation
Krzysztof Patan, Maciej Patan, Fault-tolerant design of non-linear iterative learning control using neural networks, Engineering Applications of Artificial Intelligence, 2023, vol. 124, ISSN 0952-1976 https://doi.org/10.1016/j.engappai.2023.106501 doi: https://doi.org/10.1016/j.engappai.2023.106501
CC BY - Creative Commons Attribution 4.0
Select all 18 files in this dataset.
Please select a file or files to be deleted.
The file(s) will be deleted after you click on the Delete button.
Files will not be removed from previously published versions of the dataset.
Please select a file or files to be edited.
For selected file(s) set a license to
Please select a file or files to be downloaded.
Please select a file or files for access request.
Please select restricted file(s) to be unrestricted.
You need to Log In/Sign Up to request access to this file.
Please confirm and/or complete the information needed below in order to continue.
Asterisks indicate required fields
Access to file(s) subject to additional consent under following conditions:
The restricted file(s) selected may not be downloaded because you have not been granted access.
Click Continue to download the files you have access to download.
Are you sure you want to delete this dataset and all of its files? You cannot undelete this dataset.
Are you sure you want to lift the embargo?
Once you lift the embargo, you will not be able to set it again.
Are you sure you want to delete this draft version? Files will be reverted to the most recently published version. You cannot undelete this draft.
Use a Private URL to allow those without Dataverse accounts to access your dataset. For more information about the Private URL feature, please refer to the User Guide.
Private URL has not been created.
Are you sure you want to disable the Private URL? If you have shared the Private URL with others they will no longer be able to use it to access your dataset.
You will not be able to make changes to this dataset while it is in review.
This dataset cannot be published until University of Zielona Góra is published. Would you like to publish both right now?
Once you publish this dataset it must remain published.
Are you sure you want to republish this dataset?
Select if this is a minor or major version update.
This dataset cannot be published until University of Zielona Góra is published by its administrator.
This dataset cannot be published until University of Zielona Góra and RepOD are published.
Are you sure you want to deaccession? The selected version(s) will no longer be viewable by the public.
Contact person for this dataset, having substantive knowledge of the data
Please fill this out to prove you are not a robot.
