Course Information
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Description (from WebSIS):
A learn-by-design introduction to modeling and control of discrete- and continuous-time systems, from intuition-building analytical techniques to more computational and data-centric strategies. Topics include: linear difference/differential equations (natural frequencies, transfer functions); controller metrics (stability, tracking, disturbance rejection); analytical techniques (PID, root-loci, lead-lag, phase margin); computational strategies (state-space, eigen-placement, LQR); and data-centric approaches (state estimation, regression, and identification). Concepts are introduced with lectures and online problems, and then mastered during weekly labs. In lab, students model, design, test, and explain systems and controllers involving sensors, actuators, and a microcontroller (e.g., optimizing thrust-driven positioners or stabilizing magnetic levitators). Students taking graduate version complete additional problems and labs.
Prerequisites: 8.02 (GIR) and (18.C06 or 18.06)
Labs
The learn-by-design philosophy of 6.310 centers on weekly 3-hour labs. Three alternative lab sections are available:
- Thursdays 2pm-5pm in 38-545
- Fridays 10am-1pm in 38-545
- Fridays 2pm-5pm in 38-545
Most of the learning about design comes from working on the labs. Please work with a partner. It does not have to be the same partner for each week. Students who work in pairs (NOT triplets) typically get more out of the lab experience (and have more fun) than those who stoically work alone.
The weekly labs are organized as six projects that each comprise two of the 3-hour lab sessions. The first of these sessions focuses on constructing a system to be controlled, making measurements to characterize that system, and developing a preliminary design of a controller. The second session focuses on mastering the concepts presented in lecture and practiced in the prelab. Typically, you will derive models, use those models to design controllers, test those controllers, and then analyze test results.
The design labs are challenging, and are key learning experiences. The design labs are NOT exams! We expect you to need help (from peers and/or the staff), but we also expect you to succeed, and to be able to demonstrate a comprehensive understanding during checkoff interviews. The checkoff interviews are also NOT exams! They are intended to help you stretch your understanding and uncover gaps. They can be repeated during lab and/or during office hours with NO PENALTY.
Lectures
Two weekly lectures introduce the theory that is developed in the labs.
PreLab Exercises
Online exercises provide an opportunity for you to develop an understanding of the theory that will be applied in the lab. These prelab exercises are delivered through this web site and you will receive immediate feedback about your progress. The questions are intended to help you test/verify your understanding, so incorrect answers can be resubmitted WITHOUT PENALTY. The prelab's goal is to help you develop the skills needed for the design lab, so please finish the prelab before starting the design lab.
PostLab Exercises
PostLab problems provide an opportunity to demonstrate your understanding of the lab and its underlying theory by answering more open-ended questions using a more traditional pencil-and-paper format. You can submit your solutions online in a variety of formats, including pdfs or image files of handwritten solutions (e.g. jpg or png). Solutions will be posted shortly after the due date, and students' solutions will not be accepted after the solutions are posted (see late policy below). Your solutions to postlabs are graded by the staff, typically in less than one week after submission.
Exams
There are no quizzes or final exam in this subject.
Open Lab Hours
Office hours will be available at times posted on our home page, but may be adjusted based on class preferences. Please take advantage of these times to finish labs and/or clarify your understanding of the course material.
Do not wait until the last minute to get help, as office hours can get very BUSY as due dates approach.
Grades on Assignments
Due dates for all assignments (prelabs, lab checkoffs, and postlabs) are indicated at the top of that assignment. Generally, these due dates are at 10:30pm on the Wednesay following the corresponding lab, so that you are ready to work on the new lab when it begins on Thursday or Friday.
Your grades on the online questions and in-lab checkoffs are based on percentage completed, WITHOUT REGARD TO RETRIES! Your written postlab questions will be graded using MIT's grade definitions:
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A: Exceptionally good performance demonstrating a superior understanding of the subject matter, a foundation of extensive knowledge, and a skillful use of concepts and/or materials.
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B: Good performance demonstrating capacity to use the appropriate concepts, a good understanding of the subject matter, and an ability to handle the problems and materials encountered in the subject.
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C: Adequate performance demonstrating an adequate understanding of the subject matter, an ability to handle relatively simple problems, and adequate preparation for moving on to more advanced work in the field.
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D: Minimally acceptable performance demonstrating at least partial familiarity with the subject matter and some capacity to deal with relatively simple problems, but also demonstrating serious deficiencies.
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F: Failed.
Final Grade in Undergraduate Version (6.3100)
To get an A you must
-- complete all of the checkoffs in all of the labs,
-- submit correct answers to at least 90% of prelabs,
-- receive a grade of C or higher on each of the postlabs, and
-- receive an average grade of A on the postlabs (after dropping lowest postlab score).
To get an B you must
-- complete all of the checkoffs in all of the labs,
-- submit correct answers to at least 80% of prelabs, and
-- receive a grade of C or higher on each of the postlabs, and
-- receive an average grade of B or higher on the postlabs (after dropping lowest postlab score).
To get an C you must
-- complete all of the checkoffs in at least 5 of the 6 labs.
To get an D you must
-- complete all of the checkoffs in at least 4 of the 6 labs.
Final Grade in Graduate Version (6.3102)
To get an A you must
-- satisfy all of the criteria for an A in 6.3100 and
-- receive an average grade of A on the graduate problems.
To get an B you must
-- satisfy all of the criteria for a B in 6.3100 and
-- receive an average grade of B or higher on the graduate problems.
Extension Policy
Our goal is to help you to get the most out of your educational experience in this subject and beyond. As with most subjects at MIT, this subject is fast-paced and challenging, though we try to minimize stress with our penalty-free retry policies (you never fail, you just haven't succeeded yet). Nevertheless, since we insist that you eventually demonstrate thorough understanding in each of more than thirty staff interviews, it can be difficult to catch up if you fall too far behind.
If you find yourself having difficulties or find yourself falling behind, please do not struggle alone. Discuss the situation with the staff, with your academic advisor, and/or S^3.
We will do all that we can to provide accomodations if unplanned issues (such as illness or personal problems) arise. Please consult an instructor if you have concerns.
Collaboration Policy
We encourage students to discuss 6.310 concepts and approaches with other students and with the teaching staff to better understand these materials. However, it is important that these conversation be held at a high level, and work that you submit under your name -- including derivations, programs, plots, and explanations -- must be your own. When you submit an assignment under your name, you are certifying that the details are entirely your own work and that you played at least a substantial role in the conception stage.
Students should not take credit for work done by other students. Students should not use solutions of other students (from this semester or from previous semesters) in preparing their own solutions. And students should not share their work with other students, including through public repositories such as GitHub.
Copying work, or knowingly making work available for copying, in contravention of this policy may incur reduced grades, failing the course, and/or other disciplinary action.
Weekly homework assignments provide an opportunity to develop intuition for new concepts by actively applying the new concepts to solve problems and answer questions. The process of actively struggling with the use of new ideas until you understand them is an effective and rewarding form of education. Reading someone's solution to a problem is not educationally equivalent to generating your own solution. If you skip the process of personally struggling with new concepts by getting the answers from someone else, you will have lost an important learning opportunity.
Good problems are a valuable resource. Don't squander that resource.
These policies are in place with the primary goal of helping you learn more
effectively. If you have any questions about why the policies are structured as
they are, or if a certain type of collaboration is allowed, just ask! You can
do so by sending e-mail to the instructors (6.310-instructors-fall2024@mit.edu).
For more information, see the academic integrity handbook.
