546 Wi22 -- Optimization and Learning for Control

546 Wi22 home page

- class time / office hours:  TuTh 1:00--2:20p in THO 235
- additional meetings by appointment
- contact:  please use Canvas's Conversations feature to contact Prof. Burden

Overview

The past few decades have witnessed a revolution in control of dynamical systems using computation instead of pen-and-paper analysis.  The scalability and adaptability of optimization and learning methods make them particularly powerful, but modern engineering applications involving nonclassical systems (hybrid, [human-]cyber-physical, infrastructure, decentralized / distributed, …) require generalizations of state-of-the-art algorithms.  This class will provide a unified treatment of abstract concepts, scalable computational tools, and rigorous experimental evaluation for deriving and applying optimization and (reinforcement) learning techniques to control.

Schedule

I will make lecture notes and videos available electronically on Canvas; though I will draw freely from the Syllabus references when writing my notes, I will endeavor to cite specific chapters and results in specific books and papers.

 quick links:

paper repository Links to an external site.

 JupyterHub self-assess video Links to an external site.

Week 1 (Jan 4 & 6) overview -- hw0 due 5p Fri Jan 7 

Week 2 (Jan 11 & 13) overview -- Thu Jan 13 Links to an external site.

Week 3 (Jan 18 & 20) optimization for control -- hw1 due Fri Jan 21 -- hw1sol Links to an external site. -- Tue Jan 18 Links to an external site. -- Thu Jan 20 Links to an external site.

Week 4 (Jan 25 & 27) optimization for control -- Tue Jan 25 Links to an external site. -- Thu Jan 27 Links to an external site.

Week 5 (Feb 1 & 3) learning for control -- hw2 due Fri Feb 4 -- hw2sol Links to an external site.

Week 6 (Feb 8 & 10) learning for control 

Week 7 (Feb 15 & 17) learning for control -- hw3 due Fri Feb 18

Week 8 (Feb 22 & 24) paper / project presentations

Week 9 (Mar 1 & 3) paper / project presentations

Week 10 (Mar 8 & 10) paper / project presentations

Finals week (Fri Mar 18) projects due

CC Attribution Non-Commercial This course content is offered under a CC Attribution Non-Commercial Links to an external site. license. Content in this course can be considered under this license unless otherwise noted.