Page 54 - Practical Control Engineering a Guide for Engineers, Managers, and Practitioners

Introduction to Developing Control Algorithms 29

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F1caURE 2·9 The benefits of separation: interaction, evolution, synergism, and
problem isolation.

2-4 Some General Comments about
Debugging Control Algorithms
This is a sore subject with a lot of engineers, yours truly included.
Perhaps it's best to simply tell a couple of war stories.
Rookie Fright
I joined a large manufacturing company with only a couple of years
of experience after leaving graduate school with a Ph.D. in chemical
engineering. Although I had been into a plethora of hobbies and
projects before college, my life as a professional student had been
reclusive and narrow and I had no hands-on engineering experi-
ence, nor much interest in gaining any-hey, I was an applied math-
ematician (I thought)!
When in Doubt, Simulate-Not!
Given the comments of the above paragraph, I really was good for
little other than generating sophisticated mathematical simulations.
At my previous job I had been adept at making mathematical models
of complicated processes, cooking up complicated algorithms for
controlling the model, and using the process model to show how
wonderfully the control algorithm would work.
The rationale for using simulation to develop a control algorithm
is simple and, in my opinion, quite incorrect. This approach is flawed
because the model basically contains the knowledge of the modeler
and little else. When it is put through its paces, it will surprise no one.
Furthermore, the model will likely not contain any of the subtle idio-
syncrasies of the real process-idiosyncrasies that might defeat the
control algorithm developed by using the model.
The motivation for using a mathematical model often comes from
a manager who has little actual knowledge of mathematical model-
ing. He has, however, observed that suggesting mathematical model-
ing as a solution to some difficult process problem often comes across
as an enlightened commandment.