Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors

Aidan Laracy, Hamid Ossareh
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:340-349, 2020.

Abstract

This paper provides a novel combination of Reference Governors (RG) and Iterative Learning Control (ILC) to address the issue of simultaneous learning and constraint management in systems that perform a task repeatedly. The proposed control strategy leverages the measured output from the previous iterations to improve tracking, while guaranteeing constraint satisfaction during the learning process. To achieve this, the system is modeled by a linear system with polytopic uncertainties. An RG solution based on a robust Maximal Admissable Set (MAS) is proposed that endows the ILC algorithm with constraint management capabilities. An update law on the MAS is proposed to further improve performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v120-laracy20a, title = {Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors}, author = {Laracy, Aidan and Ossareh, Hamid}, booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control}, pages = {340--349}, year = {2020}, editor = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie}, volume = {120}, series = {Proceedings of Machine Learning Research}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/laracy20a/laracy20a.pdf}, url = {https://proceedings.mlr.press/v120/laracy20a.html}, abstract = { This paper provides a novel combination of Reference Governors (RG) and Iterative Learning Control (ILC) to address the issue of simultaneous learning and constraint management in systems that perform a task repeatedly. The proposed control strategy leverages the measured output from the previous iterations to improve tracking, while guaranteeing constraint satisfaction during the learning process. To achieve this, the system is modeled by a linear system with polytopic uncertainties. An RG solution based on a robust Maximal Admissable Set (MAS) is proposed that endows the ILC algorithm with constraint management capabilities. An update law on the MAS is proposed to further improve performance.} }
Endnote
%0 Conference Paper %T Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors %A Aidan Laracy %A Hamid Ossareh %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-laracy20a %I PMLR %P 340--349 %U https://proceedings.mlr.press/v120/laracy20a.html %V 120 %X This paper provides a novel combination of Reference Governors (RG) and Iterative Learning Control (ILC) to address the issue of simultaneous learning and constraint management in systems that perform a task repeatedly. The proposed control strategy leverages the measured output from the previous iterations to improve tracking, while guaranteeing constraint satisfaction during the learning process. To achieve this, the system is modeled by a linear system with polytopic uncertainties. An RG solution based on a robust Maximal Admissable Set (MAS) is proposed that endows the ILC algorithm with constraint management capabilities. An update law on the MAS is proposed to further improve performance.
APA
Laracy, A. & Ossareh, H.. (2020). Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 120:340-349 Available from https://proceedings.mlr.press/v120/laracy20a.html.

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