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Name Modified Size InfoDownloads / Week
Low-thrust Interplanetary Trajectory Optimization 2023-02-16
Aero-assist Trajectory Optimization 2023-02-16
Parametric Analysis of Minimum TLi Delta-v Trajectories 2023-02-16
Finite-burn Translunar Trajectory 2023-01-31
Lunar Free-return Trajectory Analysis 2023-01-31
Gravity-perturbed Optimal Orbital Transfer 2023-01-30
Closest Approach between the Earth and an Asteroid 2023-01-30
Optimal Impulsive Orbital Transfer 2023-01-30
The Gravity-perturbed Hohmann Transfer 2023-01-30
N-body, Single Gravity-assist Trajectory Design 2023-01-30
Interplanetary Trajectory Correction Maneuver Optimization 2023-01-29
Finite-burn Earth Orbit Rendezvous Trajectory Optimization 2023-01-29
Continuous Low-thrust LEO-to-GEO Trajectory Optimization 2023-01-29
One Maneuver Finite-burn Orbital Transfer 2023-01-29
Re-entry Trajectory Optimization 2023-01-29
Patched-conic, Gravity-assist Trajectory Optimization 2023-01-28
Ballistic Interplanetary Trajectory Optimization 2023-01-28
Finite-burn Ascent from the Moon 2023-01-28
Finite-burn De-orbit Trajectory Optimization 2023-01-28
Finite-burn Earth Orbit to Hyperbolic Injection 2023-01-28
Two Maneuver Finite-burn Orbital Transfer 2023-01-28
Finite-burn Earth Escape 2023-01-28
readme.txt 2023-02-04 1.8 kB
Computer Methods for Aerospace Trajectory Optimization.pdf 2023-01-29 9.5 MB
Totals: 24 Items   9.5 MB 0
This project is a suite of computer programs called Computer Methods for Aerospace Trajectory Analysis (CMATO)
for solving practical problems in aerospace trajectory optimization.  All programs are written in gfortran and
utilize the Sparse Optimization Suite (SOS) software.

The zipped archive for each application contains the source code, several example simulation definition files
and a Raspberry Pi-compatible executable program. These programs were compiled using gfortran.

The Sparse Optimization Suite is a direct transcription method that can be used to solve a variety of trajectory
optimization problems using the following combination of numerical methods

• collocation and implicit integration

• adaptive mesh refinement

• sparse nonlinear programming

The CMATO software consists of gfortran routines that perform the following tasks.

• set algorithm control parameters and call the transcription/optimal control subroutine

• define the problem structure and perform initialization related to scaling, lower and upper bounds,
  initial conditions, constraints, etc.

• compute the right-hand-side differential-algebraic equations

• evaluate any point and path constraints

• display the optimal solution results and create an output file

SOS will use this information to automatically transcribe the user’s optimal control problem and perform the 
optimization using a sparse nonlinear programming (NLP) method selected by the user.  

Additional information about the mathematical techniques and numerical methods used in the Sparse Optimization
Suite can be found in the book, Practical Methods for Optimal Control Using Nonlinear Programming, Third Edition,
by John. T. Betts, SIAM, 2020.

Source: readme.txt, updated 2023-02-04