| Controller performance may be measured
by simply calculating the measurement-setpoint variance. Simple,
but of limited use as this measure depends on the underlying disturbances
of the process - the more disturbances, the larger the variance,
even if the controller model fidelity and tuning remain the same.
And it's scale dependent, which makes it difficult to scan large
number of controllers for unacceptable values.
Most controller performance software packages get around these
limitations by employing a performance index, which compares
the current variance to that which would be obtained if an "optimal"
controller had been applied to the process over the same time range.
Advantages? The disturbance effect is theoretically removed, as
both the actual controller and the optimal controller are subject
to the same disturbances. And because a ratio is taken, the number
is naturally scaled to be between zero and one.
But what is this "optimal" controller? Is it an adequate
representation of what could be applied in practice? Most
software packages use a minimum variance controller as
the optimal controller (this is the basis for the Harris Index).
This may not be a reasonable standard - a minimum variance controller
is essentially a PID controller with deadtime compensation. But
most controllers in a plant don't have deadtime compensation, and
usually don't contain derivative action (as it can be troublesome
in practice).
So comparing PI controllers to a minimum variance controller is
often not a reasonable comparison. Rather than using a minimum variance
controller, the Control Arts ControlMonitor package determines what
the variance would be if a well-tuned PI controller had
been applied to the process over the same time frame. And because
PI control is practically possible, this is a much more realistic,
stable, and accurate standard for a controller performance metric. |
