AA/EE/ME 594: Robust Control
(Autumn 2025)
AA/EE/ME 594: Robust Control
(Autumn 2025)
Course Information
Instructor: Jing Yu
TA: Hongyu Yi
Time & location: Mondays and Wednesdays 4.30am-5.50pm, MEB 238
Class Q&A: Ed Discussion
Office hours:
Jing: Thursday 4.30 - 5.30 pm (ECE 210M)
Hongyu: Tuesday 2:00 - 3:00 pm (ECE 2nd floor Atrium near ECE 230)
Course Description
This graduate-level special topics course introduces modern methods in robust control, blending classical control foundations with advanced tools from optimization, linear analysis, and system theory.
Prerequisites: This is a theory class, so it is expected that students are prepared to engage with formal derivations and proofs. Additionally, students should be comfortable with control theory concepts at the level of EE447, linear algebra at the level of Math 308, familiarity with basic machine learning and convex optimization concepts will be helpful but not required.
Learning objectives: By the end of this course, students should be able to…
Analyze and certify stability and robustness properties of linear systems under uncertainty.
Design and evaluate controllers using various robust control design tools from the class.
Interpret academic papers from the robust control research literature.
Propose and complete a short research project on the course topics.
Tentative Schedule
Grading
Two problem sets (20% x 2), Final team project 60%
Late submissions
You have four late tokens, each worth 24 hours for problem set submissions (no questions asked). Tokens may be combined and used on any homework, but not on the final project write-up or presentation. Late work without tokens will only be accepted in exceptional cases.
Final team project
The goal of the project is for you to apply and demonstrate robust control concepts (and related ideas) in a meaningful way. While you are not required to restrict your project to the topics covered in lectures, it should maintain a clear and direct connection to dynamics, control, and robustness.
Project types: You may choose from the following 2 types of projects:
Paper reading: Select a theory-oriented research paper and provide a critical analysis of its contributions. Your project should demonstrate a clear understanding of the problem the paper addresses, its main results, and the key ideas and proofs supporting them. You should also include a concise literature review to place the paper in context, explain its primary approach or insight, and discuss the significance and implications of the results.
Research project: Formulate and investigate a research question related to robust control. Your project should motivate the problem, articulate the main research objective, and outline your proposed approach, which may include theoretical analysis, simulations, or preliminary experiments.
Project proposal: The project proposal is a mechanism to get you to start thinking about your project and will serve as a starting point for your final report.
Meeting with the instructor and TA: During the week of Oct 27, you must sign up for a 10-min project meeting with the instructor and the TA to discuss your ideas for the project.
Project proposal: A project proposal (max 2 pages) is due by 11:59 PM on Monday, Nov 3, 2025. For a paper reading project, the proposal should include an overview of the research area (literature review), a description of the problem addressed in the paper, a summary of the paper’s approach or main insight, and a high-level description of the main results. For a research project, the proposal should provide a brief introduction to the research area, the motivation for the study, the main research question, and the proposed approach to the problem.
Upload your project proposal to Canvas (1 per team).
The entire team receives the same grade.
Grade components:
Project presentation (25% of the total course grade) during the week of Dec 1, 2025
Project report (35% of the total course grade) due at 11:59pm on Monday, Dec 10, 2025
There is not a specific grading rubric for the project presentation and the report. However, we will look for the following factors for both the presentation and the report:
Paper reading:
Did you clearly summarize the main problem the paper addresses? Did you explain why this problem is important in the broader field? Did you place the results in context with prior work or classical approaches?
Did you fully understand the proofs and can explain them in your own words? Did you identify the key idea/insight of the paper? Everyone learns differently- did you present additional commentary and intuition for the result and the proof? What is the most difficult part of the proof for you?
Did you try using the theorem in some way or form? For example, a paper may have an abstract theorem as the main result. You can try to specialize the theorem under a specific system/setting that hopefully simplify the result/proof. You may also include numerical experiments to explore the results in the paper, for example, to test whether certain assumptions are necessary, to examine how tight the derived bounds are, or to investigate whether the results extend beyond the stated setting (e.g., from linear to nonlinear systems).
Research project:
How much thought and consideration did you put into the research problem formulation? Is the problem setting sufficiently d challenging or interesting to explore?
How much thought and consideration did you put into your approach? Did you identify the previous approaches and their limitations? Did you attempt to address these limitations through your work? Did you note key obstacles in implementing your approach? Even if your results were not successful, did you try enough things and reflect on why they did not work? What would you have done differently given more time and resources?
How much thought and consideration did you put into your investigation? Did you use reasonable performance metrics? Did you compare with appropriate baselines? Did you provide insight and visualize key aspects of your approach (using tables, figures, toy examples)? For any theoretical components of the project, have you identified the crucial assumptions and limitations?
Project presentation:
Each team will present their project in class for 25 min with 5 min for Q&A (subject to change depending on final # teams).
Each team member must present a portion of the talk.
The presentation order will be determined randomly, with accommodations made for any scheduling conflicts during finals week.
Project report:
The report is expected to be around 6 to 10 pages (max 15 pages).
Use the NeurIPS LaTeX format and use the "preprint" option when compiling your report.
Use of GenAI
This is a graduate-level course on a special topic, and it is assumed that all students enrolled are motivated to actively engage with and learn the material. In this spirit, the use of generative AI tools is permitted, provided they are used as a support for learning.
Students are free to use generative AI in any way that enhances their understanding of the course content. To make the most of these tools, I recommend beginning interactions with a prompt such as:
“Consider yourself a teaching assistant for a robust control course. Explain and answer all my questions with an active learning style that helps me think through the problems.”
Accommodation & Accessibility
The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/). Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form (https://registrar.washington.edu/students/religious-accommodations-request/).
If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course. If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between the student, instructor, and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.