  The derivation of multi-step-ahead prediction models from sampled data of a
linear system is considered. A dedicated prediction model is built for each
future time step of interest. In addition to a nominal model, the set of all
models consistent with data and prior information is derived as well, making
the approach suitable for robust control design within a Model Predictive
Control framework. The resulting parameter identification problem is solved
through a sequence of convex programs, overcoming the non-convexity arising
when identifying 1-step prediction models with an output-error criterion. At
the same time, the derived models guarantee a worst-case error which is always
smaller than the one obtained by iterating models identified with a 1-step
prediction error criterion.
