Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session


  • 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


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

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



Homework: problem sets every other Thursday due 2 weeks later (usually) at 11 am 20%
Two midterms: both are in class, and you are allowed 1 sheet of notes (both sides) for the first, 2 sheets for the second 25% each
Final exam 30%



  • 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.


  • 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.