# Robust resampling/interpolation method

**URL:** <https://discourse.julialang.org/t/robust-resampling-interpolation-method/132665>\
**Category:** Modelling & Simulations\
**Tags:** control, controlsystems, identification\
**Created:** [September 25, 2025, 6:46pm UTC](https://discourse.julialang.org/t/robust-resampling-interpolation-method/132665 "2025-09-25T18:46:35Z")\
**Posts on this page:** 1\
**Showing post:** 8

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**Author:** ![baggepinnen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/baggepinnen/32/693_2.png) [@baggepinnen](https://discourse.julialang.org/u/baggepinnen)\
**Post date:** [September 28, 2025, 6:00am UTC](https://discourse.julialang.org/t/robust-resampling-interpolation-method/132665/8 "2025-09-28T06:00:08Z")

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> [@langestefan](#):
>
> But for the low frequency disturbance? The convention in literature seems to be to just treat it like a constant parameter and assume it stays constant in the future. Maybe you have some thoughts on that?

It’s not so much a convention as an explicit model that says it’s constant. If you use the model  
\dot{x}\_d = 0 + w  
for your low-frequency disturbance, i.e., an integrator of noise, you are explicitly modeling something that has zero deterministic dynamics (constant), only random fluctuations that are equally likely to go in any direction. There are more elaborate disturbance models that do not have zero dynamics, and in that case, simulating those forward in time (for forecasting) does not in general lead to a constant evolution.

> [@langestefan](#):
>
> methods that optimize the next-step ahead prediction diverge too quickly if I simulate longer timeseries

diverge in what sense?

> [@langestefan](#):
>
> So if I am interested in simulation performance

Whenever you have unmeasured disturbances you are typically not interested in pure simulation performance, there must be some mechanism present to account for the unmeasured disturbances, and the only information available about this is the measurements _you do have_ access to. These must feed into the dynamics somehow, which is what the filtering accomplishes. The approach from your previous post on the subject sounds spot on what you want, filtering with 24h forecast at each point, minimizing the forecasting errors along the trajectory.

> [@langestefan](#):
>
> These do not seem to be useful in the deterministic MPC case beyond estimating the initial condition, because there is no ‘correct’ step as there is no measured data

You can treat forecasted values (from e.g. a weather forecast) as “measurements” and incorporate those as any other measurement. Typically, those would have a rather large covariance compared to actual sensor measurements.

> [@langestefan](#):
>
> I am an EE

So am I, at least in the sense that I studied EE 😅

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