Using Neuromancer for Moving Horizon Estimation #85
Description
Hi!
I would like to ask for some help in the following matter.
I am trying to use Neuromancer for learning-based/differenciable moving horizon estimation in a similar fashion as it is used for the DMPC.
However, though the optimization problem of the MHE is quite similar to the MPC's in its formalization, there are some differences: here the current value of the optimization variable - the estimated state vector (x_k) - is compared to its value at a different time step (see equations - w_k).
And this part is what I could not really implement/work out how to implement in Neuromancer, as I did not find such an example where an optimization variable is used in such a way. (Or maybe I have missed it...)
So my question is: is it possible to implement this using Neuromancer 'out-of-the-box' (so without extending the functionality of any of the existing classes)? And if it is possible, could you give me some help/suggestions how to do it?
Thanks in advance!