TR2016-058
Extremum Seeking-based Iterative Learning Model Predictive Control
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- , "Extremum Seeking-based Iterative Learning Model Predictive Control", IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, DOI: 10.1109/CCA.2014.6981582, June 2016, pp. 1849-1854.BibTeX TR2016-058 PDF
- @inproceedings{Subbaraman2016jun,
- author = {Subbaraman, Anan and Benosman, Mouhacine},
- title = {Extremum Seeking-based Iterative Learning Model Predictive Control},
- booktitle = {IFAC International Workshop on Adaptation and Learning in Control and Signal Processing},
- year = 2016,
- pages = {1849--1854},
- month = jun,
- doi = {10.1109/CCA.2014.6981582},
- url = {https://www.merl.com/publications/TR2016-058}
- }
- , "Extremum Seeking-based Iterative Learning Model Predictive Control", IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, DOI: 10.1109/CCA.2014.6981582, June 2016, pp. 1849-1854.
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Abstract:
In this paper, we study a tracking control problem for linear time-invariant systems with model parametric uncertainties under input and states constraints. We apply the idea of modular design introduced in Benosman [2014], to solve this problem in the model predictive control (MPC) framework. We propose to design an MPC with input-to-state stability (ISS) guarantee, and complement it with an extremum seeking (ES) algorithm to iteratively learn the model uncertainties. The obtained MPC algorithms can be classified as iterative learning control (ILC)-MPC.