  This contribution considers one central aspect of experiment design in system
identification. When a control design is based on an estimated model, the
achievable performance is related to the quality of the estimate. The
degradation in control performance due to errors in the estimated model is
measured by an application cost function. In order to use an optimization based
input design method, a convex approximation of the set of models that atisfies
the control specification is required. The standard approach is to use a
quadratic approximation of the application cost function, where the main
computational effort is to find the corresponding Hessian matrix. Our main
contribution is an alternative approach for this problem, which uses the
structure of the underlying optimal control problem to considerably reduce the
computations needed to find the application set. This technique allows the use
of applications oriented input design for MPC on much more complex plants. The
approach is numerically evaluated on a distillation control problem.
