Enhancing statistical performance of data-driven controller tuning via L2L2-regularization

Noniterative data-driven techniques are design methods that allow optimal feedback control laws to be derived from input–output (I/O) data only, without the need of a model of the process. A drawback of these methods is that, in their standard formulation, they are not statistically efficient. In this paper, it is shown that they can be reformulated as L2L2-regularized optimization problems, by keeping the same assumptions and features, such that their statistical performance can be enhanced using the same identification dataset. A convex optimization method is also introduced to find the regularization matrix. The proposed strategy is finally tested on a benchmark example in the digital control system design.