Units: 3
Lectures: Redwood Hall, Rm. G19, Mondays and Fridays, 12:45-2:00pm
Instructor: Dimitry Gorinevsky, Packard 253, (650) 724-6783, gorin@stanford.edu
Office hours: Fridays, after the lecture.
Administrative Assistant: Denise Murphy, Packard 267, (650) 723-4731, Fax (650) 723-8473, denise@ee.stanford.edu.
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:
Course requirements:
Homework: Homework will normally be assigned each Friday and due the following Friday by 6pm 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 basic control courses such as ENGR 105 and 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. Control history and state of the art. Overview of advanced control engineering components: control algorithms, analysis, system modeling and simulation, validation and verification, identification, tuning, and diagnostics. Analysis and design steps necessary to engineer simple control loops. Practically used multivariable control approaches: model-predictive control and optimization, dynamic inversion. Practical approaches to dealing with nonlinearity: gain scheduling, nonlinear maps, neural networks. Health management: diagnostics and fault accommodation. Brief overview of further advanced control topics: estimation and navigation, adaptive control, system-level logic, integrated system design, user interface for control. Algorithms illustrated for some of the real-life applications in: high-tech, aerospace, industrial processes, automotive, telecom, and consumer appliances.