| 书目名称 | Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares |
| 编辑 | Marco Gallieri |
| 视频video | |
| 概述 | 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 |
| 丛书名称 | Springer Theses |
| 图书封面 |  |
| 描述 | 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. |
| 出版日期 | Book 2016 |
| 关键词 | Asychronous actuator interventions; LASSO Model Predictive Control; LASSO cost function; Least Absolute |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-27963-3 |
| isbn_softcover | 978-3-319-80247-3 |
| isbn_ebook | 978-3-319-27963-3Series ISSN 2190-5053 Series E-ISSN 2190-5061 |
| issn_series | 2190-5053 |
| copyright | Springer International Publishing Switzerland 2016 |
发表于 2025-3-21 18:13:30
