Information

You are not logged in.

Please Log In for full access to the web site.
Note that this link will take you to an external site (https://shimmer.mit.edu) to authenticate, and then you will be redirected back to this page.

Course Description

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.

Official Prerequisites: Physics II (GIR) and 18.06 or permission of instructor

Lecture, Recitation and Lab Schedule

Lecture/Recitation: Monday and Wednesday, 3pm -4pm, 4-231.

In 6.3100/2, we cover a curated list of topics in dynamical system modeling and control, in both continuous and discrete time. The class is design-oriented, so the majority of the learning comes from working on the labs, preferably with a partner.

Labs are on Fridays. Each lab will span two weeks, and have four to six checkoffs with a member of staff, and we would ask that you get checked off on lab before the following Tuesday of the second week. All but the final check-off can be done individually or in pairs, but we ask that the final check-off for each lab be done individually, so we can tailor the last interview more individually. Please note, interviews can be repeated, even multiple times, AT NO PENALTY.

Labs will have links to put you on check-in queues.

Office Hours

Lab and all Office Hours (see schedule in "Weekly Events" below) are in person (in 38-545) and sometimes supplemented by zoom.

Weekly Events and Due Dates

A typical two-week lab will be as follows (starting on Monday of a given week): First week:

Mon: Lec:3pm-4pm, 4-231.

Tues: Due:Prev Week's Lab Check-offs. Off. Hrs.:7pm-10pm, 38-545.

Wed: Lec:3pm-4pm, 4-231, Release: PreLab,10pm

Thur:Lab. Hrs:7pm-10pm, 38-545

Fri: Lab: 10am-1pm or 2pm-5pm, 38-545. Due: Prev Week Postlab.

Sun: Lab Hrs: 7pm-10pm, 38-545 Second week:

Mon: Lec:3pm-4pm, 4-231.

Tues: Due:Prev Week's Lab Check-offs. Off. Hrs.:7pm-10pm, 38-545.

Wed: Lec:3pm-4pm, 4-231,

Thur: Off. Hrs:7pm-10pm, 38-545

Fri: Lab: 10am-1pm, 2pm-5pm, 38-545. Due: Prelab (BEFORE LAB!) Release: PostLab,Midnight,due in one week.

Sun: Lab Hrs: 7pm-10pm, 38-545

As you can see, office hours are spread throughout the week, however DO NOT WAIT UNTIL THE LAST DAY TO DO ASSIGNMENTS. Office hours might get busy!

Grading and late policy.

Grading is based on work completed, and is non-competitive. The on-line problems allow many retries, the interviews are pass/retry, and you have a budget of 30 no-excuse-needed late days for labs and post-labs (however prelabs MUST be completed BEFORE starting each lab's second half). You can earn an A as long as you don't fall too far behind, complete 90% of the on-line problems, complete all the interviews (including final one-on-one checkoff interview for each of the labs). And for 6.3102, complete a reasonable project.

Our goal is for you to leave 6.3100/2 with the confidence and skill to model a system and design its controller, along with a context for learning more. Expect to build and model what you control, to decide on objectives and design appropriate controllers, and to be challenged but not stressed.

Staff

The names and contact information for individual staff members can be found below.

Name Role Office Email (@mit.edu) Picture
Jacob White Instructor 38-545 white Jacob
Alexandre Megretski Instructor 38-545 ameg Alexandre
Tomas Palacios Instructor 38-545 tpalacios Tomas
Joao Cavalcanti Vilela TA 38-545 caval Joao(j-oh-ow)
Tesla Wells TA 38-545 teslaw Tesla