Predictive software embedded in processors

By Control Engineering Staff March 7, 2001

Foxboro, Mass.- Embedded Connoisseur places Multivariable Predictive Control (MPC) software in fault-tolerant I/A Series Control Processors to build and deliver robust, responsive MPC solutions to fast acting processes such as combustion and surge control. I/A Series embedded Connoisseur supports developing mathematical process models, establishment of constraint limits, and automatic control of the process to maximize economic, quality, and production objectives. Foxboro www.foxboro.com

Added information I/A Series Embedded Connoisseur MPC traces its roots back to the original Connoisseur Multivariable Predictive Control package developed back in the early 1980s by Dr. David Sandoz of the University of Manchester in England. Initially, Connoisseur was commercialized by Predictive Controls Ltd. (PCL) of Norwich, England, a company Dr. Sandoz founded. With its multiple-model characteristics and adaptive multivariable control, Connoisseur was found to be uniquely applicable to industries where advanced control was not traditionally used-including food, fine chemicals, and mineral processing, as well for the more traditional ‘APC’ industries, such as refining and petrochemicals.

In 1998, The Foxboro Company, which was then part of the Siebe Control Systems division (now Invensys Process Automation), acquired PCL. Foxboro assumed both marketing and implementation responsibilities while overseeing and providing additional resources for ongoing Connoisseur development. Today, Connoisseur MPC is an integral part of Foxboro’s I/A Series Control Suite in both its host-based and embedded incarnations. Invensys Process Automation has also enjoyed considerable success implementing Connoisseur over a variety of non-Foxboro DCS and PLC control platforms, company sources say.

At a glance…

Model Predictive Control runtime with I/A Series security

Constraint management, optimization

No network engineering efforts

Stabilizes interactive multivariate processes


Related Resources