Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control

Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:1004-1014, 2021.

Abstract

The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matrix model is derived by maximum likelihood estimation with noise-corrupted data. By linearizing online, the implicit model can be used as a linear constraint to characterize possible trajectories of the system in receding horizon control. The signal matrix can also be updated online with new measurements. This algorithm can be applied to large datasets and slowly time-varying systems, possibly with high noise levels. An additional regularization term on the prediction error can be introduced to enhance the predictability and thus the control performance. Numerical results demonstrate that the proposed signal matrix model predictive control algorithm is effective in multiple applications and performs better than existing data-driven predictive control algorithm.

Cite this Paper

BibTeX
@InProceedings{pmlr-v144-yin21a, title = {Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control}, author = {Yin, Mingzhou and Iannelli, Andrea and Smith, Roy S.}, booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control}, pages = {1004--1014}, year = {2021}, editor = {Jadbabaie, Ali and Lygeros, John and Pappas, George J. and A. Parrilo, Pablo and Recht, Benjamin and Tomlin, Claire J. and Zeilinger, Melanie N.}, volume = {144}, series = {Proceedings of Machine Learning Research}, month = {07 -- 08 June}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v144/yin21a/yin21a.pdf}, url = {https://proceedings.mlr.press/v144/yin21a.html}, abstract = {The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matrix model is derived by maximum likelihood estimation with noise-corrupted data. By linearizing online, the implicit model can be used as a linear constraint to characterize possible trajectories of the system in receding horizon control. The signal matrix can also be updated online with new measurements. This algorithm can be applied to large datasets and slowly time-varying systems, possibly with high noise levels. An additional regularization term on the prediction error can be introduced to enhance the predictability and thus the control performance. Numerical results demonstrate that the proposed signal matrix model predictive control algorithm is effective in multiple applications and performs better than existing data-driven predictive control algorithm.} }
Endnote
%0 Conference Paper %T Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control %A Mingzhou Yin %A Andrea Iannelli %A Roy S. Smith %B Proceedings of the 3rd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2021 %E Ali Jadbabaie %E John Lygeros %E George J. Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire J. Tomlin %E Melanie N. Zeilinger %F pmlr-v144-yin21a %I PMLR %P 1004--1014 %U https://proceedings.mlr.press/v144/yin21a.html %V 144 %X The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matrix model is derived by maximum likelihood estimation with noise-corrupted data. By linearizing online, the implicit model can be used as a linear constraint to characterize possible trajectories of the system in receding horizon control. The signal matrix can also be updated online with new measurements. This algorithm can be applied to large datasets and slowly time-varying systems, possibly with high noise levels. An additional regularization term on the prediction error can be introduced to enhance the predictability and thus the control performance. Numerical results demonstrate that the proposed signal matrix model predictive control algorithm is effective in multiple applications and performs better than existing data-driven predictive control algorithm.
APA
Yin, M., Iannelli, A. & Smith, R.S.. (2021). Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control. Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 144:1004-1014 Available from https://proceedings.mlr.press/v144/yin21a.html.

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