## EE363: Course InformationProfessor Stephen Boyd, Stanford University, Winter Quarter 2008-09
## Lectures & section
## Office hoursStephen Boyd: Tuesdays 10:45–12 and 1:30–2:30. TAs: Tuesdays 3–6 pm, Packard 107. Wednesdays 7–9 pm, Packard 277. Thursdays 4–7 pm, Packard 277.
## Textbook and optional referencesThere 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, McGraw-Hill.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, SIAM.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 casual browsing.
## 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.
## PrerequisitesWorking knowledge of basic linear algebra, from EE263 or equivalent; basic probability and statistics, as in Stat 116 or EE278. ## Catalog descriptionA 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 control systems. 3 Units. |