Iterative learning control (ILC) algorithms enable high‐performance control design using
only approximate models of the system. To deal with severe modeling errors, a robustness …

This paper proposes a non-iterative direct data-driven technique that deals with linear time-
invariant (LTI) controller design by directly identifying the controller from input-output data …

A new model-reference data-driven approach is presented which uses the frequency
response data of a system in order to avoid the problem of unmodeled dynamics associated …

Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only
measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce …

In this paper, the problem of synthesizing a distributed controller from data is considered,
with the objective to optimize a model-reference control criterion. We establish an explicit …

Learning can substantially increase the performance of control systems that perform
repeating tasks. The aim of this letter is to develop an efficient iterative learning control …

Iterative feedback tuning (IFT) enables the tuning of feedback controllers based on
measured data without the need for a parametric model. The aim of this paper is to develop …

In this paper, the problem of tuning the attitude control system of a multirotor unmanned
aerial vehicle (UAV) is tackled and a data-driven approach is proposed. With respect to …

This paper considers data-driven distributed controller synthesis for interconnected linear
systems subject to unmeasured disturbances. The considered problem is the op-timization …

The data-driven synthesis of a distributed controller in the presence of noise is considered,
via the distributed virtual reference feedback tuning (DVRFT) framework. The analysis is …