A trajectory-based framework for data-driven system analysis and control
For control theorists and practitioners, it provides a theoretical extension of data-driven control to a class of nonlinear systems, though the extension is limited to systems linear in coordinates.
This paper extends the behavioral framework result that a single persistently exciting trajectory spans the full behavior of an LTI system to certain nonlinear systems linear in input-output coordinates, and demonstrates its application to data-driven simulation using kernel methods.
The vector space of all input-output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given that the respective input signal is persistently exciting. This fact, which was proven in the behavioral control framework, shows that a single measured trajectory can capture the full behavior of an LTI system and might therefore be used directly for system analysis and controller design, without explicitly identifying a model. In this paper, we translate the result from the behavioral context to the classical state-space control framework and we extend it to certain classes of nonlinear systems, which are linear in suitable input-output coordinates. Moreover, we show how this extension can be applied to the data-driven simulation problem, where we introduce kernel-methods to obtain a rich set of basis functions.