Data-Driven Control: Part Two of Two: Hot Take: Why not go with Models?

A recurring question that all authors of this special issue encounter is, “Why not go with models?” Two terms need to be clarified: In this context, a
model
is understood as a parametric system representation often endowed with an interpretable structure, for example, a state-space representation with a readily discernible
F
=
m
·
a
equation. Further, the term
data-driven control
, as we employ it in this special issue, is not just about using data from a black box to inform decision making. Researchers are exploring different paradigms, among others, model-based control design, where the model and uncertainty estimates are learned from data using contemporary system identification and uncertainty quantification techniques. In classical adaptive control terminology
[1]
,
[2]
, this two-stage approach is referred to as
indirect
. In contrast,
direct
data-driven control bypasses models in the decision making; see
Figure 1
for a graphical illustration of the two paradigms. Hence, the more precise question should be, “When should we embrace direct or indirect data-driven control?” I will delve into the expected “it depends” answer in this “Editorial” column.