Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares

Beschreibung

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.
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.
Proposes a novel Model Predictive Control (MPC) strategy Presents a straightforward and systematic approach to obtaining asynchronous actuator interventions Outperforms more common MPC strategies when tested on vessel roll reduction Includes supplementary material: sn.pub/extras

Autor*in

Marco Gallieri

Themen in »Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares«

Asychronous actuator interventions LASSO Model Predictive Control LASSO cost function Least Absolute Shrinkage and Selection MPC Model Predictive Control Novel MPC Strategy Operator Sparse actuation Model Predictive Control standard control techniques ℓ1-regularised least squares loss function MPC

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