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

  • Marco Gallieri

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxx
  2. Marco Gallieri
    Pages 1-8
  3. Marco Gallieri
    Pages 9-45
  4. Marco Gallieri
    Pages 47-63
  5. Marco Gallieri
    Pages 145-172
  6. Marco Gallieri
    Pages 173-183
  7. Marco Gallieri
    Pages 185-187

About this book


This thesis proposes a novel Model Predictive Control (MPC) strategy, which modifies the usual MPC cost function in order to achieve a desirable sparse actuation. It features an ℓ1-regularised least squares loss function, in which the control error variance competes with the sum of input channels magnitude (or slew rate) over the whole horizon length. While standard control techniques lead to continuous movements of all actuators, this approach enables a selected subset of actuators to be used, the others being brought into play in exceptional circumstances. The same approach can also be used to obtain asynchronous actuator interventions, so that control actions are only taken in response to large disturbances. This thesis presents a straightforward and systematic approach to achieving these practical properties, which are ignored by mainstream control theory.


Asychronous actuator interventions LASSO Model Predictive Control LASSO cost function Least Absolute Shrinkage and Selection MPC Model Predictive Control Novel MPC Strategy Operator Sparse actuation Model Predictive Control standard control techniques ℓ1-regularised least squares loss function MPC

Authors and affiliations

  • Marco Gallieri
    • 1
  1. 1.AnalyticsMcLaren Applied TechnologiesWokingUnited Kingdom

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-27961-9
  • Online ISBN 978-3-319-27963-3
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
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