On optimal input design in system identification for model predictive control

Abstract

This paper considers a method for optimal input design in system identification for model predictive control. The objective is to provide the user with a model that guarantees, with high probability, that a specified control performance is achieved. We see that, even though the system is nonlinear, using linear theory in the input design can reduce the experimental effort. The method is illustrated in a minimum power input signal design in identification of a water tank system.