From: W. Trevor King <wking@tremily.us>
Date: Fri, 14 Jun 2013 18:30:07 +0000 (-0400)
Subject: pyafm/auxiliary.tex: I can't find a source for bang-bang tuning
X-Git-Tag: v1.0~68
X-Git-Url: http://git.tremily.us/?a=commitdiff_plain;h=1fc536225afcc63bbf5215f585884de5f3ddf8fe;p=thesis.git

pyafm/auxiliary.tex: I can't find a source for bang-bang tuning

I wonder where the algorithm is from? :p
---

diff --git a/src/pyafm/auxiliary.tex b/src/pyafm/auxiliary.tex
index 8c6a082..1e90570 100644
--- a/src/pyafm/auxiliary.tex
+++ b/src/pyafm/auxiliary.tex
@@ -201,11 +201,10 @@ while\citep{ziegler42}, but finding appropriate feedback terms for
 sensitive systems is not trivial.  There are a number of tuning
 procedures which characterize the system by evaluating its response
 under simpler driving conditions.  The \pypid\ package implements
-Ziegler--Nichols' step response\citep{ziegler42}, bang-bang
-response\citep{TODO}, ultimate cycle response\citep{ziegler42} tuning
-rules, as well as Cohen--Coon's\citep{cohen53} and
-Wang--Juang--Chan's\citep{wang95} step response tuning
-rules\citep{astrom93}.
+Ziegler--Nichols' step response\citep{ziegler42}, bang-bang response,
+ultimate cycle response\citep{ziegler42} tuning rules, as well as
+Cohen--Coon's\citep{cohen53} and Wang--Juang--Chan's\citep{wang95}
+step response tuning rules\citep{astrom93}.
 
 \nomenclature{PID}{Proportional-integral-derivative feedback.  For a
   process value $p$, setpoint $p_0$, and manipulated variable $m$, the