Adaptive input design in system identification

Recently there have been significant developments in re-casting experiment design problems in system identification as convex optimization problems. The practical implementation of these methods is hampered by the fact that typically the "data" in the optimization problem depend on the to be identified system. In this contribution we propose an adaptive certainty equivalence solution based on a recursively identified model. The input design is adapted by taking one Newton step using data from the last identified model.