  This paper presents a data-driven approach to the design of predictive
controllers. The prediction matrices utilized in standard model predictive
control (MPC) algorithms are typically constructed using knowledge of a system
model such as, state-space or input-output models. Instead, we directly
estimate the prediction matrices relating future outputs with current and
future inputs from measured data, off-line. On-line, the developed data--driven
predictive controller reduces to solving a quadratic program with a similar
structure and complexity as linear MPC. Additionally, we develop a new
procedure for estimating prediction matrices from data for predictive
controllers with integral action, corresponding to the rate-based formulation
of linear MPC. The effectiveness of the developed data-driven predictive
controller is illustrated on position control of a linear motor model.
