Course presentation for online-master and industry-master
students
Model Predictive Control
IIA4117Instructor:
Roshan Sharma
University of Southeastern Norway
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Topics
• Info about the course in a nut shell
• Where to look for materials?
• How to communicate?
• Assignments, final exam, project work etc.
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IIA4117 Model Predictive Control
5 credits
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What will you learn? Mathematical model of processes
Openloop Simulation
(without control)
Linear Quadratic optimal control
(LQ)
Nonlinear optimal control
(NL)
Mathematical Optimization
problems
Efficient formulation(Sparse + Dense)
Nonlinear optimization
problem formulation
Solved using qpOASES solver
in Simulink
Solved using fmincon solver
With Linear model
With Nonlinear
model
Receding/Sliding horizon strategy
Linear MPC:Linear process modelQuadratic objective
Linear Constraints
Nonlinear MPC:nonlinear process modelobjectiveConstraints
at least one (or more) are nonlinear functions
State estimation:Why and how can we use state estimators along with MPC
(Kalman filter)
• In addition:• Multi-objective
optimization
• Utopia tracking MPC
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Where is it used?
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Homepage
http://home.usn.no/roshans/mpc
• Videos
• Lecture notes
• Exercises/Lab works
• Simulators
• Solvers to download
• Canvas is used for submission of assignment reports.
Everything you need is available in homepage(but not in Canvas)
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Communication
• Email address registered in Canvas will be used (please register the one that you use often).
• In some special cases, Skype or Skype for business can also be used for oral communication.
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Examination
Final written exam(60% weight)
3 hours duration(pen/pencil only)
Project work(40% weight)
(Application of linear MPC to a helicopter unit)
• Simulator development
• Applying on real unit
(1 or 2 days at the end of the semester.Probably in the middle of November)
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Compulsory Exercises/Labworks
(Accepted/Not accepted)
Finally,
Welcome!
Enjoy the course
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