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J. Eising

Publications and source records attributed to J. Eising.

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Stability, Contraction, and Controllers for Affine Systems

Recent developments in data-driven control have revived interest in the behavioral approach to systems theory, where systems are defined as sets of trajectories rather than being described by a specific model or representation. However, most available results remain confined to linear systems, limiting the applicability of recent methods to complex behaviors. Affine systems form a natural intermediate class: they arise from linearization, capture essential nonlinear effects, and retain sufficient structure for analysis and design. This paper derives necessary and sufficient conditions independent of any particular representation for three fundamental stability problems for affine behaviors: (i) converse Lyapunov theorems for contraction of input-output systems; (ii) implementability and existence of prescribed contractive references; and (iii) whether these references can be implemented with linear or affine feedback control. For the latter, we show that linear controllers suffice for implementing contractive closed-loop, and and affine controllers are needed for equilibrium placement.

math.OC

From Time Series to Affine Systems

The paper extends core results of behavioral systems theory from linear to affine time-invariant systems. We characterize the behavior of affine time-invariant systems via kernel, input-output, state-space, and finite-horizon data-driven representations, demonstrating a range of structural parallels with linear time-invariant systems. Building on these representations, we introduce a new persistence of excitation condition tailored to the model class of affine time-invariant systems. The condition yields a new fundamental lemma that parallels the classical result for linear systems while provably reducing data requirements. Our analysis highlights that excitation conditions must be adapted to the model class: overlooking structural differences may lead to unnecessarily conservative data requirements.

math.OC