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This paper studies finite-horizon robust tracking control for discrete-timelinear systems, based on input-output data. We leverage behavioral theory torepresent system trajectories through a set of noiseless historical data,instead of using an explicit system model. By assuming that recent output dataavailable to the controller are affected by noise terms verifying a quadraticbound, we formulate an optimization problem with a linear cost and LMIconstraints for solving the robust tracking problem without any approximations.Our approach hinges on a parameterization of noise trajectories compatible withthe data-dependent system representation and on a reformulation of the trackingcost, which enables the application of the S-lemma. In addition, we propose amethod for reducing the computational complexity and demonstrate that the sizeof the resulting LMIs does not scale with the number of historical data.Finally, we show that the proposed formulation can easily incorporate actuatordisturbances as well as constraints on inputs and outputs. The performance ofthe new controllers is discussed through simulations.

A Data-Driven Convex Programming Approach to Worst-Case Robust Tracking Controller Design