pantanplan
New member
I've been using GMAT to optimize trajectories, and so far for simple trajectories I've had success. The vary command in GMAT's optimization sequence has the following parameters that can be changed:
The other parameters I can't find much info about online, I don't understand how they affect the optimization and what they should be to achieve convergence, and I have now reached a point where I have to because I'm working on a more complex, sensitive multiple shooting-optimized script.
So my question is: how to understand the optimization parameters of GMAT and what they should be in different situations? Is there a procedure or automatic method that takes into account the scale of the optimization problem and its sensitivity, and gives an estimation of what the optimization parameters should be?
And as a secondary question: what should they be when I want GMAT to consider a wide array of possible trajectories with different values of control variables, especially when those control variables are epochs or time intervals? E.g., if I want to go from Earth to Mars, is there a way to get GMAT to propagate the spacecraft from the starting to the final destination across a large range of departure and arrival epochs, and find the lowest-delta v one, without me providing any a priori information (kind of like a more customizable patched conics approximation)?
- Initial value: The initial guess. I know the gradient descent optimization method that GMAT uses is very sensitive to initial conditions and so this must be feasible or reasonably close to the final optimal value.
- Perturbation: The step size used to calculate the finite difference derivative.
- Max step: The maximum allowed change in the control variable during a single iteration of the solver.
- Additive scale factor: Number used to nondimensionalize the independent variable. This is done with the equation xn = m (xd + a), where xn is the non-dimensional parameter, xd is the dimensional parameter and this parameter is a.
- Multiplicative scale factor: Same as above, but it's the variable d in the equation.
The other parameters I can't find much info about online, I don't understand how they affect the optimization and what they should be to achieve convergence, and I have now reached a point where I have to because I'm working on a more complex, sensitive multiple shooting-optimized script.
So my question is: how to understand the optimization parameters of GMAT and what they should be in different situations? Is there a procedure or automatic method that takes into account the scale of the optimization problem and its sensitivity, and gives an estimation of what the optimization parameters should be?
And as a secondary question: what should they be when I want GMAT to consider a wide array of possible trajectories with different values of control variables, especially when those control variables are epochs or time intervals? E.g., if I want to go from Earth to Mars, is there a way to get GMAT to propagate the spacecraft from the starting to the final destination across a large range of departure and arrival epochs, and find the lowest-delta v one, without me providing any a priori information (kind of like a more customizable patched conics approximation)?