Open AccessNon‐linear predictive generalised minimum variance state‐dependent control
Author(s) -
Grimble Michael John,
Majecki Pawel
Publication year - 2015
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.0356
Subject(s) - control theory (sociology) , model predictive control , multivariable calculus , variance (accounting) , minimum variance unbiased estimator , linear system , process (computing) , computer science , linear model , state (computer science) , limit (mathematics) , activity based costing , mathematics , control (management) , mathematical optimization , control engineering , algorithm , engineering , mean squared error , statistics , artificial intelligence , mathematical analysis , accounting , marketing , business , operating system
A non‐linear predictive generalised minimum variance control algorithm is introduced for the control of non‐linear discrete‐time state‐dependent multivariable systems. The process model includes two different types of subsystems to provide a variety of means of modelling the system and inferential control of certain outputs is available. A state‐dependent output model is driven from an unstructured non‐linear input subsystem which can include explicit transport‐delays. A multi‐step predictive control cost function is to be minimised involving weighted error, and either absolute or incremental control signal costing terms. Different patterns of a reduced number of future controls can be used to limit the computational demands.