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Principles of LASSO MPC

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Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares

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Abstract

Least Absolute Selection and Shrinking Operator (LASSO) is a powerful regression technique, which aims to induce sparseness in the solution (Tibshirani 1996; Osborne and Presnell 2000; Kim et al. 2007a; Ohlsson 2010; Schmidt 2010; Schuet 2010).

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Notes

  1. 1.

    This results in the \(\ell _{asso}\)-MPC problem to have a unique solution.

  2. 2.

    The notion of system state is slightly abused here, since u is memory-less and directly manipulated.

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Authors and Affiliations

  1. Analytics, McLaren Applied Technologies, Woking, UK

    Marco Gallieri

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  1. Marco Gallieri
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Correspondence to Marco Gallieri .

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Gallieri, M. (2016). Principles of LASSO MPC. In: Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-27963-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-27963-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27961-9

  • Online ISBN: 978-3-319-27963-3

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