.. extensions.append('sphinx.ext.pngmath')

.. role:: raw-html(raw)
    :format: html

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The Self-Tuning PID Controllers Toolbox
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**This toolbox contains the self-tuning PID controllers** published in:

- Jérôme Mendes, Luís Osório, and Rui Araújo. **Self-tuning pid controllers in pursuit of plug and play capacity**. *Control Engineering Practice*, 69:73--84, December 2017. `[doi] <https://doi.org/10.1016/j.conengprac.2017.09.006>`_  

**Abstract:** This work addresses the problem of controlling unknown and time varying plants for industrial applications. The concept of “plug-and-play” was pursued using control algorithms that auto-adapt their control parameters in order to control unknown and time-varying plants. Self-Tuning Controllers (STC) with PID form were studied and tested on a real process setup. The setup is composed of two coupled DC motors and a variable load. Controllers’ performances were compared in order to distinguish which controllers perform better, which are easier to set up, which have a better initial response, and which enable faster reaction to plant variations and load disturbances.


**Software source code:** `The Self-Tuning PID Controllers Toolbox <\_toolboxes/cep2017_v69Dec_73_toolbox_1.0.zip>`_ (Scilab implementation)


How to Run
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For use with Scilab:
	- **MainFile_Controllers.sce** – Just run the file.