Excitation of control-affine systems and Koopman error bounds
For researchers using data-driven Koopman methods for control-affine systems, this work provides a principled input selection strategy to improve robustness, though it is incremental in nature.
This paper addresses data requirements for bilinear EDMD in control-affine systems by proposing a framework for input selection based on subspace angles to ensure a minimal singular value threshold, and derives optimality conditions for maximizing this value. The approach is demonstrated on nonholonomic robots.
The Koopman operator and extended dynamic mode decomposition (EDMD) as a data-driven technique for its approximation have attracted considerable attention as a key tool for modeling, analysis, and control of complex dynamical systems. However, extensions towards control-affine systems resulting in bilinear surrogate models are prone to demanding data requirements rendering their applicability intricate. In this paper, we propose a framework for data-fitting of control-affine mappings to increase the robustness margin in the associated system identification problem and, thus, to provide reliable bilinear EDMD schemes. In particular, guidelines for input selection based on subspace angles are deduced such that a desired threshold with respect to the minimal singular value is ensured. Moreover, we derive necessary and sufficient conditions of optimality for maximizing the minimal singular value. Further, we demonstrate the usefulness of the proposed approach using bilinear EDMD with control for nonholonomic robots.