EE363: Course Information
Lectures & section
Lectures: Tuesdays and Thursdays, 9:30–10:45 am, 200-034 (Northeast
corner of main Quad).
Problem session: Tuesdays, 5:15–6:05 pm, Hewlett 103,
every other week. There will be problem sessions on
2/10/09, 2/24/09, …
Textbook and optional references
There is no textbook.
Everything we’ll use is posted on the 363 website in pdf format.
If you’d like to consult some books, we listed some below.
LQR and Kalman filtering are covered in many books on linear
systems, optimal control, and optimization. One good one is
Dynamic Programming and Optimal Control, vol. 1,
Bertsekas, Athena Scientific. Another two are Optimal Filtering and Optimal
Control: Linear Quadratic Methods, both
Anderson & Moore, Dover.
Lyapunov theory is covered in many texts on linear systems,
e.g., Linear Systems, Antsaklis & Michel,
Nonlinear Lyapunov theory is covered in most texts on
nonlinear system analysis, e.g., Nonlinear systems: Analysis,
Stability, and Control, Sastry, Springer, or
Nonlinear Systems Analysis (2nd edition), Vidyasagar,
Lots of material on LMIs can be found in Boyd, El Ghaoui,
Feron, and Balakrishnan,
Linear Matrix Inequalities
in System and Control Theory, but this is not a book for
Course requirements and grading
Class attendance. We mean it.
Weekly homework assignments.
Homework will normally be assigned each Thursday
and due the following Friday by 5 pm in the inbox outside Denise’s
office, Packard 267.
Late homework will not be accepted.
You are allowed, even encouraged, to work on the homework in
small groups, but you must write up your own homework to hand in.
Final exam (24 hour take home),
tentatively scheduled for March 12, 13, or 14.
Grading: Homework 25%, final 75%. These
weights are approximate; we reserve the right to change them
Working knowledge of basic linear algebra, from EE263 or equivalent;
basic probability and
statistics, as in Stat 116 or EE278.
A continuation of EE263.
Optimal control and dynamic programming; linear quadratic regulator. Lyapunov
theory and methods. Time-varying and periodic systems. Realization
theory. Linear estimation and the Kalman filter. Examples and
applications from digital filters, circuits, signal processing, and