  In this paper, a novel machine learning derived control performance assesment
(CPA) classification system is proposed. It is dedicated for a class of
PID-based control loops with processes exhibiting second order plus delay time
(SOPDT) dynamical properties. The proposed concept is based on deriving and
combining a number of different, diverse control performance indices (CPIs)
that separately do not provide sufficient information about the control
performance. However, when combined together and used as discriminative
features of the assessed control system, they can provide consistent and
accurate CPA information. This concept is discussed in terms of the introduced
extended set of CPIs, comprehensive performance assessment of different machine
learning based classification methods and practical applicability of the
suggested solution. The latter is shown and verified by practical application
of the proposed approach to a CPA system for a laboratory heat exchange and
ditribution setup.
