Information about this course
From ControlsWiki
Instructors:
Robert Ziff, Rm. 3312 G. G. Brown, Tel: (734) 764-5498
Email: rziff at umich dot edu
Barry Barkel. Rm. 3010 H. H. Dow
Email: bmbarkel (at) umich.edu
GSIs:
Yu Chen
Email: yuch (at) umich.edu
Trung Nguyen
Email: ndtrung (at) umich.edu
Class Time: Tuesday, Thursday: 10:00 -11:30 pm, room 1013 Dow Building
Office Hour Wednesday, 5:30 ~7:-- pm, ChE cof room,(3074) Dow Building
Course Description
As a practicing chemical engineer, you will be faced with the task of doing things reliably in an uncertain world and with imperfect understanding. In this course we will show you a variety of approaches to reduce or manage this uncertainty through the use of robust designs, dynamic systems theory, nonlinear dynamics, control theory, and statistics.
This course will provide a multi-faceted approach to learning. Class will meet during scheduled time for lectures, demonstrations, class problems, and discussions. Lectures recorded by the previous instructor (Prof. Peter Woolf) will be available as supplementary material. There is no required textbook; the material and all class information will be available on a "wiki" site, which allows all registered users to make changes and additions. This site is freely available online at http://controls.engin.umich.edu (or just Google "controlswiki"). Computational aspects of the course will take advantage of numerical software, especially the Mathematica program available on campus computers.
Lectures for the first three weeks will be given by Barry Barkel, a retired practicing engineer with many years of experience in the practice of process design and control. Mr. Barkel's part of the course will emphasize P & I D ("Piping and Instrumentation Diagram") and practical control strategies. The remainder of the course will be taught by Robert Ziff, who has been at the University of Michigan for over 20 years, and taught control for several years in the early 00's.
Objectives
At the conclusion of this course you should be able to:
- Describe a process, how it works, and what your control objectives are
- Instrument a process
- Describe processes with appropriate diagrams
- Numerically model a process from physical and logical models.
- Fit a model to data.
- Understand feed-forward, feed-back, and PID control of systems
- Tune process controllers
- Understand the principles behind multi-objective control architectures
- Predict product quality range for a process
- Identify sensitivities in process models
Web Site
This course has two websites. The first is a University of Michigan CTools website If you have already signed up for the course, then the appropriate courses should already be selected for you. Otherwise, search for “process control” to find the course. On this site we will post additional reading material and much of the paperwork of the course.
The second website is the wiki textbook site hosted here. To edit this site, you will need to sign up for an account to the site. This restricted account will allow only members of the class to edit and upload content on the site.
Textbook & Reading Assignments
There is no textbook purchase required for this course—the text is the wiki. The course projects, exams, quizzes, and homeworks will all be derived from this source. In addition, lectures will be linked from the text for viewing online or download. Students are responsible for all material in the wiki readings and lectures.
Note that this class has a lot of reading, and you will not be able to digest all of the material in detail without reading this material. Your job in this class is to keep up with the readings and to learn the concepts behind the topic.
Wiki Edits
Part of your grade will depend on improving the content on the wiki. Click here for criteria for receiving credit and examples of wiki edits that received credit.
Schedule and Organizational Documents
Other texts that may also be of interest
- ECOSSE HyperCourse on control theory from the university of Edinburgh, Scotland
- David MacKay's text on Information Theory, Inference, and Learning Algorithms. This text provides a solid foundation for probability theory.
- Video lectures on linear algebra from MIT's open courseware. If you want to learn more about eigenvalues, eigenvectors, determinants, and SVD, here is a good place to go!
