Moving-Horizon Predictive Input Design for Closed-Loop Identification

This paper presents a new approach to input design for closed-loop identification. The idea is to maximize the trace of the Fisher information matrix associated with the plant model, while enforcing explicit constraints on both inputs and outputs. The result is the richest possible excitation signal for which the operation of a running closed-loop system remains within acceptable bounds. The function to be maximized is a convex quadratic. A Moving Horizon Predictive (MHP) framework is used to solve the input design problem at each sample time. The method can be combined with a fixed model variable regressor technique to estimate time delays. The suggested technique is implemented and used to identify machine-directional processes in an industrial paper machine.