Dual System Representation and Prediction Method for Data-Driven Estimation

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for a prediction of states and a calculation of an observer gain. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represents the original system. Linear matrix inequality that depends on data of the dual system provides the observer gains.