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.
| ISBN: | 9783319279619 |
| Publication date: | 11th April 2016 |
| Author: | M Gallieri |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Hardback |
| Pagination: | 187 pages |
| Series: | Springer Theses |
| Genres: |
Automatic control engineering Cybernetics and systems theory Computer modelling and simulation |
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.
Lasso-MPC - Predictive Control With L1-Regularised Least Squares features in the following genres: Automatic control engineering, Cybernetics and systems theory, Computer modelling and simulation
Lasso-MPC - Predictive Control With L1-Regularised Least Squares is available in Hardback
Lasso-MPC - Predictive Control With L1-Regularised Least Squares was written by M Gallieri and published by Springer an imprint of Springer International Publishing
Lasso-MPC - Predictive Control With L1-Regularised Least Squares has 187 pages
Yes it is part of Springer Theses series