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

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 ma...

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Bibliographic Details
Main Author: Gallieri, Marco (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:Springer Theses, Recognizing Outstanding Ph.D. Research,
Subjects:
Control engineering.
System theory.
Control theory.
Computer simulation.
Control and Systems Theory.
Systems Theory, Control.
Computer Modelling.
Online Access:Full-text access
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Description
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 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.
Physical Description:XXX, 187 p. 64 illus., 54 illus. in color. online resource.
ISBN:9783319279633
ISSN:2190-5061
DOI:10.1007/978-3-319-27963-3

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