Non-linear predictive generalised minimum variance state-dependent control

• Published in: IET control theory & applications 2015-10, Vol.9 (16), p.2438-2450
• Main Authors: Grimble, Michael John, Majecki, Pawel
• Format: Article
• Language: English
• Subjects: absolute control signal costing terms; computational demands; Control systems; Control theory; Cost engineering; delays; discrete time systems; discrete‐time state‐dependent multivariable systems; explicit transport‐delays; generalised minimum variance state‐dependent control; incremental control signal costing terms; inferential control; Mathematical models; multistep cost function; Multivariable; multivariable control systems; nonlinear control systems; nonlinear predictive control; Nonlinearity; predictive control; process model; unstructured nonlinear input subsystem; Variance
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2015.0356

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.