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

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Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares
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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Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares is written by Marco Gallieri and published by Springer. The Digital and eTextbook ISBNs for Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares are 9783319279633, 3319279637 and the print ISBNs are 9783319279619, 3319279610.