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 ma...
Main Author: | Gallieri, Marco (Author) |
---|---|
Corporate Author: | SpringerLink (Online service) |
Format: | Electronic eBook |
Language: | English |
Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
|
Series: | Springer Theses, Recognizing Outstanding Ph.D. Research,
|
Subjects: | |
Online Access: | Full Text via HEAL-Link |
Similar Items
-
Distributed Model Predictive Control Made Easy
Published: (2014) -
Intelligent Control A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms /
by: Siddique, Nazmul
Published: (2014) -
Fractional Order Differentiation and Robust Control Design CRONE, H-infinity and Motion Control /
by: Sabatier, Jocelyn, et al.
Published: (2015) -
Modelling and Identification with Rational Orthogonal Basis Functions
Published: (2005) -
System Identification, Environmental Modelling, and Control System Design
Published: (2012)