Invited Presentation at ETHZ IfA Coffee Talk Seminar

The talk abstract is given below and if you have interest, please refer to this paper.

Due to the recent advances in sensing, communication, and computation, data availability for control design is steadily increasing. This has motivated a renewed interest in system analysis and control design methods relying on finite-length data sequences. Several recent works propose using raw measurements to represent discrete-time systems and solve system analysis and control design problems. The main feature of these approaches is to bypass explicit system identification usually required in standard control design. Moreover, data-based system representations can be easier to update when new data are available, facilitating the deployment of adaptive control systems. We study finite-horizon robust tracking control for discrete-time linear systems in this work, based on input-output data. We leverage behavioral theory to represent system trajectories through a set of noiseless historical data instead of using an explicit system model. By assuming that recent output data available to the controller are affected by noise terms verifying a quadratic bound, we formulate an optimization problem with a linear cost and LMI constraints for solving the robust tracking problem without any approximations. In addition, we propose a method for reducing the computational complexity and demonstrate that the size of the resulting LMIs does not scale with the number of historical data. We also show that the proposed formulation can easily incorporate actuator disturbances and constraints on inputs and outputs.