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A Lecture on Model Predictive Control

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. HJB)(00xu =?Popularity of Quadratic Objective in Control Quadratic objective Fairly general State regulation Output regulation Setpoint tracking Unconstrained linear least squares problem has an analytical solution.

2. Conduct plant test: Vary MV’s and DV’s & record the response of CV’s 3. Derive a dynamic model from the plant test data 4. Configure the MPC controller and enter initial tuning parameters 5. Test the controller off-line using closed loop simulation 6. Download the configured controller to the destination machine and test the model

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