Iterative Learning with State-Dependent Riccati Equation

These files present learning and error reduction with the SDRE controller augmented by an iterative learning control approach.

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Updated 17 Jun 2022

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The codes are named by the order of the sections of the following report:
S. R. Nekoo, J. Á. Acosta, G. Heredia and A. Ollero, "A PD-type state-dependent Riccati equation with iterative learning augmentation for mechanical systems," in IEEE/CAA Journal of Automatica Sinica, doi: 10.1109/JAS.2022.105533.
In the codes, a transformation of the conventional SDRE to a PD-type has been presented which could be seen in Section 3.
The codes of Section 5 are for simulations and the codes of Section 6 are for experiments. To run the experiment codes, having a setup with Arduino digital board is necessary, equipped with optical encoder feedback. To run the experiment code, load the “.mat” data file first and plot the results.
The details, formulations, and more information could be found in the above reference. A video of the experiments could be seen on the journal website in the media section.
Please download the data “.mat” file for the last code from the journal’s website.

Cite As

S. R. Nekoo, J. Á. Acosta, G. Heredia and A. Ollero, "A PD-type state-dependent Riccati equation with iterative learning augmentation for mechanical systems," in IEEE/CAA Journal of Automatica Sinica, doi: 10.1109/JAS.2022.105533.

MATLAB Release Compatibility
Created with R2022a
Compatible with any release
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Version Published Release Notes
1.0.2

Text update.

1.0.1

Update.

1.0.0