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small doc correction
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docs/src/public/state_estim.md

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@@ -18,7 +18,8 @@ error with closed-loop control (offset-free tracking).
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The estimators are all implemented in the predictor form (a.k.a. observer form), that is,
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they all estimates at each discrete time ``k`` the states of the next period
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``\mathbf{x̂}_k(k+1)``. In contrast, the filter form that estimates ``\mathbf{x̂}_k(k)`` is
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``\mathbf{x̂}_k(k+1)`` (also written ``\mathbf{x̂}_{k+1|k}`` [elsewhere](https://en.wikipedia.org/wiki/Kalman_filter)).
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In contrast, the filter form that estimates ``\mathbf{x̂}_k(k)`` is
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sometimes slightly more accurate.
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The predictor form comes in handy for control applications since the estimations come after

src/controller/linmpc.jl

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The ``\mathbf{ΔU}`` vector includes the manipulated input increments ``\mathbf{Δu}(k+j) =
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\mathbf{u}(k+j) - \mathbf{u}(k+j-1)`` from ``j=0`` to ``H_c-1``, the ``\mathbf{Ŷ}`` vector,
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the output predictions ``\mathbf{ŷ(k+j)}`` from ``j=1`` to ``H_p``, and the ``\mathbf{U}``
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the output predictions ``\mathbf{ŷ}(k+j)`` from ``j=1`` to ``H_p``, and the ``\mathbf{U}``
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vector, the manipulated inputs ``\mathbf{u}(k+j)`` from ``j=0`` to ``H_p-1``. The
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manipulated input setpoint predictions ``\mathbf{R̂_u}`` are constant at ``\mathbf{r_u}``.
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src/estimator/kalman.jl

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@@ -283,7 +283,7 @@ It implements the time-varying Kalman Filter in its predictor (observer) form :
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```math
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\begin{aligned}
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\mathbf{M}(k) &= \mathbf{P̂}_{k-1}(k)\mathbf{Ĉ^m}'
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[\mathbf{Ĉ^m P̂}_{k-1}(k)\mathbf{Ĉ^m + R̂}]^{-1} \\
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[\mathbf{Ĉ^m P̂}_{k-1}(k)\mathbf{Ĉ^m}' + \mathbf{R̂}]^{-1} \\
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\mathbf{K}(k) &= \mathbf{Â M(k)} \\
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\mathbf{ŷ^m}(k) &= \mathbf{Ĉ^m x̂}_{k-1}(k) + \mathbf{D̂_d^m d}(k) \\
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\mathbf{x̂}_{k}(k+1) &= \mathbf{Â x̂}_{k-1}(k) + \mathbf{B̂_u u}(k) + \mathbf{B̂_d d}(k)
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```math
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\begin{aligned}
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\mathbf{M}(k) &= \mathbf{P̂}_{k-1}(k)\mathbf{Ĥ^m}'(k)
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[\mathbf{Ĥ^m}(k)\mathbf{P̂}_{k-1}(k)\mathbf{Ĥ^m}(k) + \mathbf{R̂}]^{-1} \\
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[\mathbf{Ĥ^m}(k)\mathbf{P̂}_{k-1}(k)\mathbf{Ĥ^m}'(k) + \mathbf{R̂}]^{-1} \\
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\mathbf{K}(k) &= \mathbf{F̂}(k) \mathbf{M}(k) \\
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\mathbf{ŷ^m}(k) &= \mathbf{ĥ^m}\Big( \mathbf{x̂}_{k-1}(k), \mathbf{d}(k) \Big) \\
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\mathbf{x̂}_{k}(k+1) &= \mathbf{f̂}\Big( \mathbf{x̂}_{k-1}(k), \mathbf{u}(k), \mathbf{d}(k) \Big)

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