From e51eba8b47faae8dd35b0b7695e61cf9fb11229b Mon Sep 17 00:00:00 2001 From: Nao Pross Date: Mon, 16 Aug 2021 09:46:07 +0200 Subject: Start something I won't ever make it in time but whatever --- tex/lti.tex | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 tex/lti.tex (limited to 'tex/lti.tex') diff --git a/tex/lti.tex b/tex/lti.tex new file mode 100644 index 0000000..b01a31f --- /dev/null +++ b/tex/lti.tex @@ -0,0 +1,89 @@ +\section{LTI systems} + +\subsection{Properties} +Let \(\mathcal{S}\) denote a system. +\begin{table}[H] + \begin{tabularx}{\linewidth}{p{.3\linewidth} X} + \toprule + \bfseries Property & \bfseries Meaning \\ + \midrule + static \(\leftrightarrow\)\newline dynamic & Static means that it is memoryless (in the statistical sense), whereas dynamic has memory. Static systems depend only on the input \(u\), dynamic systems on \(du/dt\) or \(\int u\,dt\). \\ + causal \(\leftrightarrow\)\newline acausal & Causal systems use only informations from the past, i.e. \(h(t < 0) = 0\). Real systems are always causal. \\ + linear \(\leftrightarrow\)\newline nonlinear & The output of a linear system does not have new frequency that were not in the input. For linear system the superposition principle is valid: \(\mathcal{S}(\alpha_1 x_1 + \alpha_2 x_2) = \alpha_1 \mathcal{S} x_1 + \alpha_2 \mathcal{S} x_2\). \\ + time invariant \newline\(\leftrightarrow\) time variant & Time invariant systems do not depend on time, but for ex. only on time differences. \\ + \midrule + SISO, MIMO & Single input single output, multiple input multiple output. \\ + BIBO & Bounded input bounded output, i.e. there are some \(A\), \(B\) such that \(|x| < A\) and \(|y| < B\) for all \(t\), equivalently \(\int_\mathbb{R} |h|\,dt < \infty\).\\ + \bottomrule + \end{tabularx} +\end{table} + +\subsection{Impulse response} +%% TODO: impulse response + +\subsection{Stability} +Let \(\mathcal{S}\) be a system with impulse response \(h(t)\) and transfer function \(H(s)\). +\begin{table}[H] + \centering + \begin{tabularx}{\linewidth}{lX} + \toprule + Stable & All poles are on the LHP\footnote{Left half plane, where \(\mathrm{Re}(s) < 0\).}. \\ + Marginally stable & There are no poles in the RHP but a simple pole on the \(j\)-axis. \\ + Instable & There are poles in the RHP or poles of hider order on the \(j\)-axis. \\ + \bottomrule + \end{tabularx} +\end{table} +\subsection{Distortion} +\subsection{Stochastic inputs} + +\iffalse +\begin{figure} +\begin{tikzpicture}[ + system/.style = {draw, thick, inner sep = 4mm, outer sep = 1mm} + ] + \matrix[row sep=3mm, column sep=1cm] (M) { + \node (x) {\(x(t)\)}; & + \node (g) {\(g(t) = y_\delta (t)\)}; & + \node (y) {\(y(t) = g(t) * x(t)\)}; \\ + + & + \node (h) {\(h(t)\)}; & + \node (yw) {\(y_\omega(t) = h(t) * x(t)\)}; \\ + + \node (in) {Input}; & + \node[system, fill=white] (sys) {LTI-System \(\mathcal{S}\)}; & + \node (out) {Response}; \\ + + \node (X) {\(X(s)\)}; & + \node (G) {\(G(s) = 1/p(s)\)}; & + \node (Y) {\(Y(s) = G(s) \cdot X(s)\)}; \\ + + \node (Xw) {\(X(\omega)\)}; & + \node (H) {\(H(\omega) = G(j\omega)\)}; & + \node (Yw) {\(Y_\omega (\omega) = H(\omega) \cdot X(\omega)\)}; \\ + }; + + \draw[thick, ->] (in) to (sys); + \draw[thick, ->] (sys) to (out); + + \begin{pgfonlayer}{background} + \coordinate (T1) at ($(x.north west) - (.8,-.1)$); + \coordinate (T2) at ($(yw.south east) + (.8,-.1)$); + + \coordinate (B1) at ($(X.north west) - (0,-.1)$); + \coordinate (B2) at ($(Y.south east) + (0,-.1)$); + + \coordinate (F1) at ($(Xw.north west) - (0,-.1)$); + \coordinate (F2) at ($(Yw.south east) + (0,-.1)$); + + \fill[color=blue!20] (T1) rectangle (T2); + \fill[color=magenta!20] (B1 -| T1) rectangle (B2 -| T2); + \fill[color=red!20] (F1 -| T1) rectangle (F2 -| T2); + % \fill[top color=blue!20, bottom color=magenta!20] + % (T1) rectangle (B2); + \end{pgfonlayer} +\end{tikzpicture} +\end{figure} +\fi + + -- cgit v1.2.1