The
model quality for a model predictive control (MPC) is critical
for the control loop performance. Thus, assessing the effect of model–plant
mismatch (MPM) is fundamental for performance assessment and monitoring
the MPC. This paper proposes a method for evaluating model quality
based on the investigation of closed-loop data and the nominal output
sensitivity function, which facilitates the assessment procedure for
the actual closed-loop performances. The effectiveness of the proposed
method is illustrated by a multivariable case study, considering linear
and nonlinear plants.
Stiction
is a well-known villain in industry because of the limit-cycle
imposed on the controller. Several methodologies are reported in the
literature to automatically detect this problem using only normal
operating data. However, this becomes more difficult when the loop
with stiction is affected by disturbances or the sticky valve is inside
a cascade loop. This study proposes two methods to automatically diagnose
valve stiction when the reference signal is variable and centers primarily
on recognizing triangular or sinusoidal patterns. The first method
is based on the slope of the signal peaks and the second on data segmentation.
These techniques were compared to a curve-fitting method, providing
similar results when the reference is fixed. However, for processes
significantly affected by disturbances or when the sticky valve was
inside a cascade loop, stiction detection was better for both methods
proposed. These results are corroborated by simulation and industrial
data.
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