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 8c6a082ad4ae2b4a3cccbf25a066494135745a4f..1e905703a38084a10e6508f39daaa0d88fea87e2 100644 (file)
--- 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