Experiment Design for Closed-loop Performance Diagnosis

Abstract

This paper uses prediction error identification to distinguish control-relevant system changes in closed-loop operation from variations in disturbance characteristics. The approach consists of a hypothesis test to verify whether an identified model of the true system lies in a set containing all models that exhibit adequate closed-loop performance. To increase the detection probability, i.e. the probability of choosing the correct hypothesis, experiment design is performed to devise an excitation signal for closed-loop identification of the system dynamics. For a given identification cost, this allows us to maximize the probability that an identified model of the system lies in the performance-related region of interest in accordance with the hypothesis test and, therefore, decrease the probability of opting for an erroneous hypothesis.