Data-Driven Control

The steady drive for digitization of engineering processes has greatly increased the amount of available system input-output data. At the same time, the system complexity has also increased, hindering the model derivation from first principles. This has motivated a change in the manner in which the control design is approached. Instead of modeling the system by using first principles and then proceeding to the control design, the new paradigm is to take advantage of the available data to directly characterize a controller, whose structure is typically assumed known. This approach is referred to as data-driven control.

Although this movement has its roots in computer science, where, among other techniques, neural networks, fuzzy systems, online optimization, learning methods, etc., are used for system control, the area of data-driven control has recently shifted towards the development of controller synthesis approaches, which are based on more conventional control strategies. A main reason for this is the need of rigorous guarantees on the system operation, in other words, the need of robust controllers. With this premise in mind, our current research activities in this area comprise:

  • Robust control of linear systems
  • Time-delay systems
  • Nonlinear systems



Selected publications

J. G. Rueda-Escobedo, E. Fridman, and J. Schiffer, "Data-Driven Control for Linear Discrete-Time Delay Systems", IEEE Transactions on Automatic Control, 2021

J. G. Rueda-Escobedo, and J. Schiffer, "Data-Driven Internal Model Control of Second-Order Discrete Volterra Systems", 59th IEEE Conference on Decision and Control (CDC), Jeju Island, Republic of Korea, pp. 4572-4579, 2020