From dc63534446febb9b288d92000ab27cdd1eec0605 Mon Sep 17 00:00:00 2001 From: Edoardo Morassutto Date: Thu, 4 Jun 2020 21:57:07 +0200 Subject: [PATCH] Typo --- lectures/2020-05-12.tex | 2 +- lectures/2020-05-14.tex | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/lectures/2020-05-12.tex b/lectures/2020-05-12.tex index 3d91a31..199a9c6 100644 --- a/lectures/2020-05-12.tex +++ b/lectures/2020-05-12.tex @@ -178,7 +178,7 @@ \section{Linear Time Invariant Systems} Let's consider a simplified case (SISO system with one state) to understand the approach. \[ S: \begin{cases} - x(t+1) = zx(t) + bu(t) + v_1(t) \\ + x(t+1) = ax(t) + bu(t) + v_1(t) \\ y(t) = cx(t) + v_2(t) \end{cases} \] diff --git a/lectures/2020-05-14.tex b/lectures/2020-05-14.tex index 6fe4ff4..ef1a5c5 100644 --- a/lectures/2020-05-14.tex +++ b/lectures/2020-05-14.tex @@ -364,7 +364,7 @@ \section{Non-linear Systems} $r_1(t)$ and $r_2(t)$ are the primary regressors, directly linked to $\dot{z}(t)$. $r_3(t)$ is a secondary regressor, it can help $r_1(t)$. - Since these regressors are obtained by integration to avoid drifting (by DC components of noise integration) we have to high-pass the inputs with high-pass filters ($\frac{z-1}{z-a}$). + Since these regressors are obtained by integration to avoid drifting (by DC components of noise integration) we have to high-pass the inputs with high-pass filters $\left(\frac{z-1}{z-a}\right)$. \paragraph{Full filtering scheme} \phantom{lol} \begin{figure}[H]