Search arXiv⌕ Search

arXiv · 2609.39948

Input-to-state stability of second-order port-Hamiltonian systems under nonlinear dynamic boundary feedback

Abstract

The input-to-state stability of a class of infinite-dimensional second-order port-Hamiltonian systems on a one-dimensional spatial domain is analyzed under nonlinear dynamic boundary feedback and boundary disturbances. Using energy methods in the port-Hamiltonian framework, we obtain sufficient conditions that guarantee uniform input-to-state stability of the closed-loop system.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bouchra Elghazi, Birgit Jacob, Christophe Prieur. 2026-09-30. Input-to-state stability of second-order port-Hamiltonian systems under nonlinear dynamic boundary feedback. https://arxiv.org/abs/2609.39948

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

On parallel machine scheduling with variable energy consumption functions

Manufacturing facilities face increasing challenges due to renewable energy integration and regulated pricing, motivating energy-efficient scheduling strategies. This paper studies a scheduling problem in which jobs with time-varying energy consumption must be assigned to parallel machines over a discrete planning horizon, subject to per-period energy limits, with the objective of minimizing total energy costs. A time-indexed mixed-integer linear programming (MILP) formulation that accommodates variable energy consumption profiles is proposed, thereby extending commonly used constant-consumption models. To address larger instances, a matheuristic based on Iterated Local Search is developed, implementing a Variable Neighborhood Descent combining adaptations of classical local search operators and leveraging the MILP formulation to explore large neighborhoods. Computational experiments demonstrate that the proposed approach consistently produces solutions of high quality under variable energy consumption profiles.

math.OC↗

Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization

We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-linearizable functions. Our approach leverages decentralized optimization techniques with the flexibility of upper-linearizable function frameworks, effectively generalizing traditional DR-submodular function optimization. We obtain the regret of $O(T^{1-θ/2})$ with communication complexity of $O(T^θ)$ and number of linear optimization oracle calls of $O(T^{2θ})$ for decentralized upper-linearizable function optimization, for any $0\le θ\le 1$. This approach allows for the first results for monotone up-concave optimization with general convex constraints and non-monotone up-concave optimization with general convex constraints. Further, the above results for first order feedback are extended to zeroth order, semi-bandit, and bandit feedback.

math.OC↗

Douglas--Rachford for multioperator comonotone inclusions with applications to multiblock optimization

We study the doubly relaxed Douglas--Rachford (DR) algorithm for solving a multioperator inclusion problem involving the sum of maximally comonotone operators. To address such problems, we adopt a product space reformulation that accommodates nonconvex-valued operators, which is essential when dealing with weakly comonotone mappings. We establish the convergence of the doubly relaxed DR algorithm under comonotonicity assumptions, subject to suitable conditions on the algorithm parameters and the comonotonicity moduli of the operators. Our analysis relies on the Attouch--Théra duality framework, which enables the study of convergence through the corresponding dual inclusion problem. As an application, we derive a multiblock ADMM-type algorithm for structured convex and nonconvex optimization problems by applying the doubly relaxed DR algorithm to the operator inclusion formulation of the KKT system. The resulting method extends the classical duality between the DR algorithm and the alternating direction method of multipliers from the convex two-block case to multiblock and nonconvex settings. Moreover, we establish convergence guarantees in both the fully convex and strongly convex-weakly convex regimes.

math.OC↗