Lectures: Mudd Chemistry Building, Braun Lecture Hall , Mondays and Wednesdays, 2:30-3:45pm
Instructor: Dimitry Gorinevsky, Packard 253, (650) 724-6783, email@example.com
Office hours: Wednesdays, 4 pm to 5:30 pm, Packard 253
Administrative Assistant: Denise Murphy, Packard 267, (650)
723-4731, Fax (650) 723-8473, firstname.lastname@example.org.
Teaching Assistant: Sikandar Samar, Packard 243, (650) 723-9833, email@example.comTA office hours & location: Friday 4:00-6:00pm in Packard 107
Contact information: The primary medium of interaction will
be the class newsgroup su.class.ee392m.
Instructions on how to connect to Stanford newsgroups using Microsoft
Outlook are here.
We encourage the use of newsgroup, but for personal matters you can send email to firstname.lastname@example.org. This forwards your email to the professor and the TA.
Textbook and optional references: There is no textbook. Complete lecture notes will be available in Adobe acrobat (pdf) from the class web page, www.stanford.edu/class/ee392m. Several texts can serve as auxiliary or reference texts:
Homework: Homework will normally be assigned Monday or Wednesday and due in a week (respectively Monday or Wednesday) by 5pm 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. Homework will involve some Matlab programming. Homework will be graded roughly, perhaps on a scale of 1-4.
Grading: Homework 25%, midterm 35%, final 40%. These weights are approximate; we reserve the right to change them later.
Prerequisites: Knowledge of linear algebra (EE263, Math 103, 133), signal and systems (EE102), and one or more basic control courses such as ENGR 105/ENGR 205. Ability to program in Matlab. Exposure to modeling and simulation of dynamical systems, numerical optimization, and application fields very helpful, but not a pre-requisit.
Catalog description: Concentrates on computing and analysis algorithm aspects of control engineering. Emphasizes simpler methods used in the majority of practical applications. Control history and state of the art. Overview of control engineering components: control algorithms, analysis, system modeling and simulation, validation and verification, identification, tuning, and diagnostics. Analysis and design steps necessary to engineer a simple control loop. Simple practical multivariable control: model-predictive control and optimization. Health management: diagnostics and fault accommodation. Algorithms illustrated for some of the real-life applications in: high-tech, computing, aerospace, industrial processes, automotive, telecom, and consumer appliances.