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* Advanced regulatory control (ARC) refers to several proven advanced control techniques, such as feedforward, override or adaptive gain. ARC is also a catch-all term used to refer to any customized or non-simple technique that does not fall into any other category. ARCs are typically implemented using function blocks or custom programming capabilities at the DCS level. In some cases, ARCs reside at the supervisory control computer level.
* Multivariable [[Model predictive control]] (MPC) is a popular technology, usually deployed on a supervisory control computer, that identifies important independent and dependent process variables and the dynamic relationships (models) between them, and uses matrix-math based control and optimization algorithms, to control multiple variables simultaneously. One requirement of MPC is that the models must be linear (i.e. they must be repeatable at all points of the operating range of the controller). MPC has been a prominent part of APC ever since supervisory computers first brought the necessary computational capabilities to control systems in the 1980s.
* Inferential control: The concept behind inferentials is to calculate a stream property from readily available process measurements, such as temperature and pressure, that otherwise would require either an expensive and complicated online analyzer or periodic laboratory analysis. Inferentials can be utilized in place of actual online analyzers, whether for operator information, cascaded to base-layer process controllers, or multivariable controller CVs.
* Sequential control refers to dis-continuous time and event based automation sequences that occur within continuous processes. These may be implemented as a collection of time and logic function blocks, a custom algorithm, or using a formalized [[Sequential function chart]] methodology.
* Compressor control typically includes compressor anti-surge and performance control.
* Nonlinear MPC: Similar to Multivariable MPC in that it incorporates dynamic models and matrix-math based control; however, it does not have the requirement for model linearity. Nonlinear MPC is capable of accomodating processes with models that have varying process gains and dynamics (i.e. dead-times and lag times).<ref>http://www.aspentech.com/products/advanced-process-control/aspen-nonlinear-controller/</ref>
== Related Technologies ==
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