Robust Gain-Scheduled Estimation With Dynamic D-Scalings

We provide a convex solution to the robust gain-scheduled estimation problem based on integral quadratic constraints with dynamic D-scalings for both the uncertain and the scheduled component. This closes an important gap since, so far, merely static scalings could be used for the scheduled component in estimation problems. To this end, we provide novel synthesis criteria in terms of linear matrix inequalities for the design of nominal gain-scheduled estimators that allow for a direct combination with available results on robust estimation. We illustrate the benefit of our design approach by means of a numerical example.

Automated summary

A convex solution to the robust gain-scheduled estimation problem with integral quadratic constraints and dynamic D-scalings is provided in this work, closing an important gap in current methods. Novel synthesis criteria in terms of linear matrix inequalities are introduced for the design of nominal gain-scheduled estimators, which can be directly combined with existing results on robust estimation. The benefits of this design approach are illustrated through a numerical example.