Trajectory Design for Space Systems (EN.530.626)

Course Description

We will place a heavy emphasis on optimization-based techniques and study optimal control formulations for solving trajectory optimization and model predictive control problems. Applications of interest will include interplanetary trajectory optimization, rocket entry-descent-landing, asteroid proximity operations, and planetary rover path planning. A strong emphasis will be placed on practical applications through coding implementations in Python and evaluation in simple simulation environments. Finally, a course project will be included to allow students to gain further experience on an algorithm or application of their choice.

Instructors

Prof. Abhishek Cauligi
Office: Hackerman 117

Course Assistants

Meeting Times

Lectures will be held on Tuesdays and Thursdays from 1:30-2:45PM.

Office Hours

Syllabus

The syllabus for the course can be found here.

Schedule

Week Date Topics Covered Notes Suggested Readings
1 08/26 Intro: linear algebra & differential equations review    
  08/28 Linear systems theory HW1 Released  
2 09/02 Nonlinear optimization theory    
  09/04 Constrained optimization (Pt. 1) HW1 Due, HW2 Released  
3 09/09 Pontryagin’s maximum principle and indirect methods    
  09/11 Constrained optimization (Pt. 2)    
4 09/16 Constrained optimization (Pt. 3) Form project groups  
  09/18 Constrained optimization (Pt. 4) HW2 Due, HW3 Released  
5 09/23 Planetary entry, descent, and landing    
  09/25 The two-body problem (Pt. 1) Final project proposal due  
6 09/30 The two-body problem (Pt. 2)    
  10/02 Optimum orbital transfer HW3 Due, HW4 Released  
7 10/07 Rigid bodies and Euler’s equation    
  10/09 Planning with attitude    
8 10/14 Sampling-based motion planning    
  10/16 Derivative-free methods for trajectory optimization HW4 Due, HW5 Released  
9 10/21 Surface rover path planning    
  10/23 Long and short range planner hierarchies    
10 10/28 Uncertainty propagation    
  10/30 Stochastic optimal control (Pt. 1) HW5 Due, HW6 Released  
11 11/04 Stochastic optimal control (Pt. 2)    
  11/06 Midterm Exam    
12 11/11 Learning value functions (Pt. 1)    
  11/13 Learning value functions (Pt. 2) HW6 Due  
13 11/18 Guest lecture (TBD)    
  11/20 Guest lecture (TBD)    
14 11/25 No Lecture (Fall Break)    
  11/27 No Lecture (Fall Break)    
15 12/02 Final project presentations    
  12/04 Final project presentations