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Lasso-MPC -- predictive control with L1-regularised least squares

Author: M Gallieri
Publisher: Switzerland : Springer, 2016.
Series: Springer theses.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: M Gallieri
ISBN: 9783319279633 3319279637 3319279610 9783319279619
OCLC Number: 945771678
Notes: "Doctoral thesis accepted by the University of Cambridge, UK."
Description: 1 online resource (xxx, 187 pages) : illustrations (some color).
Contents: Introduction --
Background --
Principles of LASSO MPC --
Version 1: `1-Input Regularised Quadratic MPC.- Version 2: LASSO MPC with stabilising terminal cost --
Design of LASSO MPC for prioritised and auxiliary actuators --
Robust Tracking with Soft-constraints --
Ship roll reduction with rudder and fins --
Concluding Remarks.
Series Title: Springer theses.
Responsibility: Marco Galleri.

Abstract:

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.  Read more...

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