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
|NUMBER OF LECTURES||TOPICS|
|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|
- 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.