Minimum-norm input reconstruction for nonminimum-phase systems

Input reconstruction is a process where the inputs to a system are estimated using the measured system output and knowledge of the system model. One way to achieve this goal is to invert the system model and cascade delays to guarantee that the inverse is proper. A standing issue in input reconstruction lies in the inversion of nonminimum-phase systems, since the inverse model is unstable. In the present paper we assume that the unknown input is bounded and we present a novel technique that uses Markov parameters to determine the unknown state that minimizes the difference between the unknown input and the estimated input.