Lasso-MPC - Predictive Control with 1-Regularised Least Squares (Hardcover, 1st ed. 2016)

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 1-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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Theses
Release date:	April 2016
First published:	2016
Authors:	Marco Gallieri
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Hardcover
Pages:	187
Edition:	1st ed. 2016
ISBN-13:	978-3-319-27961-9
Categories:	Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Computing & IT > Computer software packages > Computer graphics software > General Promotions
LSN:	3-319-27961-0
Barcode:	9783319279619

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