README.md

Optimal Spacecraft Landing

This mini project is based on the 13.2 Optimal spacecraft landing from Additional Exercises forConvex Optimization.

Original Exercise

CVXPY formulation

CVXPY allows to formulate and solve this problem with just a few lines of code.

import cvxpy as cp
import numpy as np

h = 1.
g = 0.1
m = 10.
Fmax = 10.
p0 = np.array([50,50,100])
v0 = np.array([-10,0,-10])
alpha = 0.5
gamma = 1.
e3 = np.array([0,0,1])
K = 35

# CVXPY problem setup
p = cp.Variable((3,K)) # position vectors
v = cp.Variable((3,K)) # velocity vectors
f = cp.Variable((3,K)) # thrust vectors

fuel = 0
for k in range(K):
    fuel = fuel + gamma * h * cp.norm(f[:,k], 2)

objective = cp.Minimize(fuel)
# Initial state
c_init = [
    p[:,0] == p0,
    v[:,0] == v0,
]
# Target
c_end = [
    p[:,-1] == 0,
    v[:,-1] == 0,
]
# Maximal thrust
c_fmax = [cp.norm(f[:,k], 2) <= Fmax  for k in range(K)]
# Glide cone. The spacecraft must remain in this region
c_cone = [p[2,k] >= alpha * cp.norm(p[:2,k], 2) for k in range(K)]
# Spacecraft dynamics constraints
c_vel = [v[:,k+1] == v[:,k] + h/m*f[:,k] - h*g*e3 for k in range(K-1)]
c_pos = [p[:,k+1] == p[:,k] + h/2*(v[:,k] + v[:,k+1]) for k in range(K-1)]

constraints = c_init + c_end + c_fmax + c_cone + c_vel + c_pos
prob = cp.Problem(objective, constraints)
res = prob.solve(verbose=True)

CVXPY converts this problem into a Second-Order Cone Program (SOCP) which is then solved by ECOS solver.

The resulting trajectory and thrust vectors can be plotted with matplotlib: