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polimi-cheatsheet · Jun 4, 2020 · dc63534 · dc63534
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diff --git 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
@@ -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]