Lasso-MPC -- predictive control with L1-regularised Least squares
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Bibliographic Information
Lasso-MPC -- predictive control with L1-regularised Least squares
（Springer theses : recognizing outstanding Ph. D. research）
Springer, 2016
- : softcover
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"Doctoral thesis accepted by the University of Cambridge, UK"--T.p
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of 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.
by "Nielsen BookData"