103 lines
6.5 KiB
Markdown
103 lines
6.5 KiB
Markdown
# Notes on Research Papers
|
|
|
|
## Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers
|
|
|
|
- For the most part this paper isn't *that* relevant.
|
|
- It seems to be a low-fidelity method, or at least, it's fidelity is kind of hard to pin down,
|
|
since it uses a neural net.
|
|
- However, the neural net is an interesting concept.
|
|
- And in fact, this paper advocates for a concept of using the neural net as a predictor. So, say,
|
|
given a leg of a particular journey, as in the Englander paper, could I use this technique rather
|
|
than Sims-Flanagan transcription in the outer loop?
|
|
- Something to consider as an alternative to the method proposed in Englander
|
|
- Could also be used to generate initial guesses for the single-shooting methods that I'll have to
|
|
use if I use an indirect optimization method.
|
|
|
|
## Design and optimization of interplanetary low-thrust trajectory with planetary aerogravity-assist maneuver
|
|
|
|
- I didn't realize that this paper is specifically talking about *aero*gravity assists. I don't
|
|
think that level of complication is necessary for this paper. I'm going to stick with gravity
|
|
assists that don't get into atmospheric effects.
|
|
|
|
## Orbital and Angular Motion Construction for Low Thrust Interplanetary Flight
|
|
|
|
- This one actually isn't even about optimization. Not relevant
|
|
- To be honest, I don't actually understand it very well anyway
|
|
|
|
## A Rapid Estimation for Interplanetary Low-Thrust Trajectories Using Support Vector Regression
|
|
|
|
- This is another machine-learning approach
|
|
- It uses a different approach that I'm not that familiar with (Support Vector Regression)
|
|
- It looks like this is a form of regression similar to linear regression, I suppose being used
|
|
by the machine learning algorithm for predicting optimal trajectories
|
|
- However, it seems like everything that applies in the neural net paper probably apply here as well
|
|
- This could be an alternative for predicting optimal trajectories over certain legs of the journey
|
|
|
|
## Automated Solution of the Low-Thrust Interplanetary Trajectory Problem
|
|
|
|
- This is the Englander paper I mentioned earlier. It seems highly relevant as they're essentially
|
|
doing what I'd like to do: producing an automated method for high-level interplanetary low-thrust
|
|
mission design including the flybys.
|
|
- I need to look up MALTO and GALLOP (established tools that do this)
|
|
- This paper also open sources it's code: available [here](https://opensource.gsfc.nasa.gov/projects/emtg/index.php)
|
|
- This paper formulates the problem as a *Hybrid Optimal Control Problem*. This requires some
|
|
further research by me, but from the paper it seems to be a way of optimizing two seperables
|
|
subproblems where one of the subproblems exists as a sub-loop of the other problem (for instance
|
|
optimizing flybys in a high-level loop and particular planet-to-planet trajectories as a sub-loop of
|
|
that problem). This apparently works because the "outer-loop" uses discrete variables while the
|
|
"inner-loop" uses continuous variables.
|
|
- The outer loop is based on the "null-gene" transciption from another Englander paper and uses a
|
|
genetic algorithm.
|
|
- I'm not going to go too deep here into the details of the GA. But there's an entire paper on
|
|
it
|
|
- The paper does mention that it lends itself well to parallelization, which is true. Kubernetes
|
|
cluster?
|
|
- The inner loop uses Sim-Flanagan transcription combined with Monotonic Basin Hopping, a method for
|
|
single-shooting without initial guesses
|
|
- Sims-Flanagan transcription is where you discretize the flight arc into many smaller time
|
|
steps and the thrust applied is approximated as an impulsive thrust in the middle of each time
|
|
step.
|
|
- SFT is considered to be a "medium-fidelity" approach
|
|
- For this solver, the trajectory betweens these points is produced as a solution to Kepler's
|
|
problem, which is basically just the analytical solution to the 2BP, so that no derivatives or
|
|
orbit-propagation is needed, for speed.
|
|
- One thing I noted about this approach is that it doesn't seem to include a possibility for
|
|
"coasting arcs" or throttling anything less than 100% (though the modeling of what 100% means is
|
|
quite thorough), so perhaps we're missing some fidelity there?
|
|
- I think SNOPT is used to optimize these "inner-loops". This should be pretty fast since it
|
|
just uses Kepler's eq
|
|
- The MBH method eliminates the need to solve Lambert's problem for initial guesses. This allows
|
|
for a more robust analysis of the search base (if there are global optima further from the local
|
|
optima near lambert's solution) but might be slower? I'm not sure.
|
|
- The technique is kind of weird, but I suppose it works.
|
|
- This paper uses a hierarchy of events starting with the overall *mission*, which separates into
|
|
*journies*, which, in the example I'm pursuing will be Earth -> Neptune and Neptune -> Earth (if
|
|
applicable, but probably just the first one). Then these journies are further divided into
|
|
*phases*, which include each planet -> planet leg. The number of phases and the identities of the
|
|
planets are chosen by the algorithm.
|
|
- The paper goes into some length to determine what the launch C3, propulsion, power, and ephemeris
|
|
modeling are. This is all very useful, but as far as I can tell it's pretty typical, so I won't note
|
|
too much about it.
|
|
- However, it does mention that SPICE presented some challenges for using a preferable method of
|
|
parallelization. As an alternative, the paper mentions that FIRE could be used instead for
|
|
ephemeris. Which might be worth looking into.
|
|
- The paper also include pseudocode, which is nice
|
|
|
|
## Automated Mission Planning via Evolutionary Algorithms
|
|
|
|
- This is another Englander paper that gives more details on the outer-loop GA. Useful for details.
|
|
|
|
## Multi-Objective Low-Thrust Interplanetary Trajectory Optimization Based on Generalized Logarithmic Spirals
|
|
|
|
- This is the first paper I looked at. It's actually quite similar to the Englander paper, but I
|
|
think not quite as good
|
|
- Again, it formulates the problem as an HOCP.
|
|
- However, the "inner-loop" for this problem is an optimization of generalized logarithmic spirals.
|
|
I don't think this is a very high fidelity method.
|
|
- The outer step uses collocation and an NLP optimizer (looks like it actually might just feed the
|
|
guesses into GALLOP, which I assume uses SNOPT, though, to be honest, I can't find much on it from
|
|
a quick search)
|
|
- I'm leaning toward Englander's approach over this one, perhaps with an alternative being to use
|
|
one of the machine-learning approaches from above for the inner loop instead
|
|
|