Courses:

Principles of Optimal Control >> Content Detail



Syllabus



Syllabus



Objectives


  • Nonlinear optimization – MATLAB implementation
  • Optimization approaches: dynamic programming, Calculus of Variations
  • Linear quadratic and H compensators – stochastic and deterministic
  • Investigate key basic control concepts and extend to advanced algorithms (MPC)
  • Will focus on both the technique/approach and the control result


Approximate Number of Lectures per Topic




Keywords


LQR = linear-quadratic regulator
LQG = linear-quadratic Gaussian
MPC = model predictive control


NUMBER OF LECTURESTOPICS
2Nonlinear optimization
3Dynamic programming
2Calculus of variations – general
3Calculus of variations – control
5LQR/LQG - stochastic optimization
3H and robust control
2On-line optimization and control (MPC)



Grades



ACTIVITIESPERCENTAGES
Homework: problem sets every other Thursday due 2 weeks later (usually) at 11 am20%
Two midterms: both are in class, and you are allowed 1 sheet of notes (both sides) for the first, 2 sheets for the second25% each
Final exam30%



Prerequisites


  • Course assumes a good working knowledge of linear algebra and differential equations. New material will be covered in depth in the class, but a strong background will be necessary.
  • Solid background in control design is best to fully understand this material, but not essential.
  • Course material and homework assume a good working knowledge of MATLAB.


Policies


  • You are encouraged to discuss the homework and problem sets. However, your submitted work must be your own.
  • Late homework will not be accepted unless prior approval is obtained from Professor How. Grade on all late homework will be reduced 25% per day. No homework will be accepted for credit after the solutions have been handed out.

 








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