From 1fc536225afcc63bbf5215f585884de5f3ddf8fe Mon Sep 17 00:00:00 2001 From: "W. Trevor King" Date: Fri, 14 Jun 2013 14:30:07 -0400 Subject: [PATCH] pyafm/auxiliary.tex: I can't find a source for bang-bang tuning I wonder where the algorithm is from? :p --- src/pyafm/auxiliary.tex | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) 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 -- 2.26.2