Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2303.15297

On the use of Lagrange Multiplier State-Space Substructuring in dynamic substructuring analysis

Rafael Da Silva Oliveira Dias·Milena Martarelli·Paolo Chiariotti
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this article, the formulation of Lagrange Multiplier State-Space Substructuring (LM-SSS) is presented and extended to directly compute coupled displacement and velocity state-space models. The LM-SSS method is applied to couple and decouple state-space models established in the modal domain. Moreover, it is used together with tailored postprocessing procedures to eliminate the redundant states originated from the coupling and decoupling operations. This specific formulation of the LM-SSS approach made it possible to develop a tailored coupling form, named Unconstrained Coupling Form (UCF). UCF just requires the computation of a nullspace and does not rely on the selection of a subspace from a nullspace. By exploiting a numerical example, LM-SSS was compared with the Lagrange Multiplier Frequency Based Substructuring (LMFBS) approach, which is currently widely recognized as a reference approach. This was done both in terms of: a)coupled FRFs derived by coupling the state-space models of two substructures and b) decoupled FRFs derived by decoupling the state-space model of a component from the coupled model. LM-SSS showed to be suitable to compute minimal order coupled models and UCF turned out to have similar performance as other coupling forms already presented to the scientific community. As for the decoupling task, the FRFs derived from the LM-SSS approach perfectly matched those obtained by LM-FBS. Moreover, it was also demonstrated that the elimination of the redundant states originated from the decoupling operation was correctly performed. The approaches discussed were exploited on an experimental substructuring application. LM-SSS resulted to be a reliable SSS technique to perform coupling and decoupling operations with state-space models estimated from measured FRFs as well as to provide accurate minimal-order models.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rafael Da Silva Oliveira Dias, Milena Martarelli, Paolo Chiariotti. 2023-02-06. On the use of Lagrange Multiplier State-Space Substructuring in dynamic substructuring analysis. https://doi.org/10.1016/j.ymssp.2022.109419

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

KEEP EXPLORING

Related papers

arXiv2026

Failure-Aware Iterative Learning of State-Control Invariant Sets

In this paper, we address the problem of computing maximal state-control invariant sets for deterministic linear systems using failing trajectories. We introduce the concept of state-control invariance, which extends control invariance from the state space to the joint state-control space. The maximal state-control invariant (MSCI) set simultaneously encodes the maximal control invariant set (MCI) and, for each state in the MCI, the set of control inputs that preserve invariance. We prove that the state projection of the MSCI is the MCI and the state-dependent sections of the MSCI are the admissible invariance-preserving inputs. Building on this framework, we develop a Failure-Aware Iterative Learning (FAIL) algorithm for deterministic linear time-invariant systems with polytopic constraints. The algorithm iteratively updates a constraint set in the state-control space by learning predecessor halfspaces from one-step failing state-input pairs, without knowing the dynamics. For each failure, FAIL learns the violated halfspaces of the predecessor of the constraint set by a regression on failing trajectories. We prove that the learned constraint set converges monotonically to the MSCI. Numerical experiments on a double integrator system validate the proposed approach.

eess.SY↗
arXiv2026

Consensus and Synchronization of Multi-agent Systems over Finite Fields - Graph Topologies

This paper presents cooperative protocols for multi-agent systems with agents having a finite state-space. Both scalar single-integrator consensus and general LTI system synchronization are considered. Systems having a finite state-space describe agents with minimal memory capacity processing only a finite alphabet. Such systems are remarkably resilient to communication noise. The crucial problem, however, is to construct the admissible communication topology, which is NP-hard. We address this by efficiently exploring the subsets of admissible graph matrices and propose two new algorithms to generate them. Simulations validate the proposed approach.

eess.SY↗
arXiv2026

Extracting Exact Lie Derivatives Without Backpropagation: A Dual Compiler for Neural Control Barrier Functions

A safety filter based on a neural control barrier function (CBF) deployed in an embedded control loop evaluates, at each control cycle, the trained network and its Lie derivatives along the system vector fields, under the memory and worst-case execution time (WCET) constraints that safety-oriented coding standards impose. Reverse-mode automatic differentiation, by which training frameworks obtain these derivatives, retains an activation cache whose size grows with the sum of the layer widths, and general-purpose differentiation runtimes allocate the computational graph from the heap at each call. This paper presents a compiler that evaluates a neural CBF and its exact Lie derivatives by forward-mode dual-number arithmetic. The compiler emits self-contained C++ code in which a single forward pass, without backpropagation, returns the barrier value and its exact Lie derivative along a given vector field; the drift and input Lie derivatives of the safety constraint are obtained from one such pass per vector field, and a second-order extension based on hyper-dual numbers returns the exact second-order Lie derivatives required by CBFs of relative degree two. The dual forward pass requires a workspace bounded by four times the widest layer, independent of network depth, and the emitted code contains no allocation call sites, so the absence of dynamic allocation is verifiable by inspection of the code. On an ESP32-S3 microcontroller, the compiled filter assembles the complete safety constraint in under one millisecond from statically allocated buffers of at most 768 bytes, and the maximum execution time over 1000 evaluations lies within 5% of the median in all three examples, whereas a heap-allocating reverse-mode baseline shows maxima 33% and 70% above its median in the two first-order examples. The compiler and the embedded experiments are released as open-source software.

eess.SY↗