External Models for Output Regulation based on Moment Estimation from Input-Output Data

In this paper, we propose a novel, data-driven approach to external model-based regulation for uncertain plant and known exosystem models. At the core of the method lies a technique for least-square estimation of the gains of the plant model at the frequency of excitation, which is adopted for the construction of a hybrid external model of an equivalent disturbance acting at the plant input. Interestingly, in spite of residual errors on the estimates (arising from the use of finite estimation intervals), the reset mechanism employed in the hybrid external model ensures asymptotic regulation, instead of practical regulation. Furthermore, the method does not require a priori knowledge of the transfer matrix of the plant, and takes advantage of an external approach to robust regulation, where the ensuing stabilization problem may be simpler than the ones typically found in internal model-based design.