Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

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 LECTURES TOPICS
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)

Grades

ACTIVITIES PERCENTAGES
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%

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.

Course Info

Learning Resource Types

assignment Problem Sets
grading Exams
notes Lecture Notes
assignment Programming Assignments