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020 _a9783319279633
040 _aES-MaUEC
050 4 _aTJ217.6
_b.G355 2016 EB
100 1 _aGallieri, Marco
_998314
_0Local
245 1 0 _aLasso-MPC :
_bPredictive Control with ℓ1-Regularised Least Squares
_cby Marco Gallieri
260 _aCham
_bSpringer International Publishing
_c2016
300 _a1 recurso en línea (XXX, 187 p.)
_b64 ilustraciones, 54 ilustraciones col.
336 _aTexto (visual)
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research
_x2190-5053
520 _aThis 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.
650 7 _aControl automático
_0comprobar BNE19900978127
_2embne
_9405125
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research
_x2190-5053
_9133434
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-27963-3
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901 _ai9783319279633
907 _a.b12946709
_b10-10-17
_c21-11-16
942 _2lcc
_cLE
945 _aTJ217.6 .G355 2016 EB
_g1
_ieBOOK
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_z06-04-17
988 _aEBOOK, EBSPRINGER
998 _am
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_b22-03-17
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