Transcription of A Lecture on Model Predictive Control - CEPAC
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A Lecture on Model Predictive Control - CEPAC
A Lecture on Model Predictive ControlJay H. LeeSchool of Chemical and Biomolecular EngineeringCenter for Process Systems EngineeringGeorgia Inst. of TechnologyPrepared for Pan American Advanced Studies Institute Program on Process Systems EngineeringSchedule Lecture 1: Introduction to MPC Lecture 2: Details of MPC Algorithm and Theory Lecture 3: Linear Model IdentificationLecture 1 Introduction to MPC- Motivation- History and status of industrial use of MPC- Overview of commercial packagesKey Elements of MPC Formulation of the Control problem as an (deterministic) optimization problem On-line optimization Receding horizon implementation (with feedback update)()()),(0,,min10iiiiiipiiiiuuxFxux guxi= += Repeat! theas solution Implement ynumericall problemon optimizati theSolveState)Current (Estimated Set ,At 00uxxktk==Eqn.
• Majority of applications (67%) are in refining and petrochemicals. Chemical and pulp and paper are the next areas. • Many vendors specializing in the technology – Early Players: DMCC, Setpoint, Profimatics – Today’s Players: Aspen Technology, Honeywell, Invensys, ABB • Models used are predominantly empirical models developed
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