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Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares [electronic resource] / by Marco Gallieri.

By: Gallieri, Marco [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Theses, Recognizing Outstanding Ph.D. Research: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: XXX, 187 p. 64 illus., 54 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319279633.Subject(s): Control engineering | System theory | Control theory | Computer simulation | Control and Systems Theory | Systems Theory, Control | Computer ModellingAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 629.8312 | 003 Online resources: Click here to access online
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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