Study of moment-based MPC formulations and their connection to classical control

Rongkai Zhang, M.B. Saltik, L. Özkan

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Model predictive control (MPC) is a well-established modern control technology used in diverse applications to provide (sub)optimal operating conditions while incorporating safety and performance constraints. In MPC, the control action at the current time instant is obtained by solving a finite horizon optimal control problem according to the forecasts of the future process behavior. Hence the quality/validity of predictions generated from process models determine the performance of these controllers ([1]). Although models based on first principles are expected to provide better predictions for a wider range of operating conditions ([2]), the model predictions should also incorporate the effects of uncertain deviations to overcome the adverse effects. To this end, robust model predictive control techniques are developed in order to reduce the effect of uncertainty ([3,4]) in dynamical processes.
Original languageEnglish
Title of host publicationComputing and Systems Technology Division 2017 - Core Programming Area at the 2017 AIChE Annual Meeting
PublisherAmerican Institute of Chemical Engineers (AIChE)
Pages388-390
Number of pages3
ISBN (Electronic)978-1-5108-5799-5
Publication statusPublished - 1 Jan 2017
EventComputing and Systems Technology Division 2017 - Core Programming Area at the 2017 AIChE Annual Meeting - Minneapolis, United States
Duration: 29 Oct 20173 Nov 2017

Conference

ConferenceComputing and Systems Technology Division 2017 - Core Programming Area at the 2017 AIChE Annual Meeting
CountryUnited States
CityMinneapolis
Period29/10/173/11/17

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