Design of experiments

System identification consists in finding a model of an unknown system starting from a finite set of noise-corrupted data. Fundamental problems in this context are to:

  1. Asses the model accuracy that can be obtained from a certain set of data;
  2. Design experiments in such a way to guarantee a maximum model accuracy, limiting at the same time the cost (in terms of time and money) of the experiments.

This activity is devoted to investigate these two problems. A novel algorithm is being developed, based on the so-called Nonlinear Set Membership theory, allowing the designer to define a minimum length experiment, which provides a “highly informative” set of data, leading to the identification of models with reduced uncertainty.

ERC Sector

  • PE7_1 Control engineering
  • PE7_3 Simulation engineering and modelling
  • PE7_10 Robotics


  • System identification
  • Design of experiment

Research groups