IFAC-PapersOnLine最新文献

英文中文

A latent representation of brain networks based on EEG⁎ 基于脑电图⁎的大脑网络潜在表征

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.08.564

Lucia Falconi , Giulia Cisotto , Mattia Zorzi

Electroencephalography (EEG) is one of the most popular techniques to investigate normal as well as pathological cerebral mechanisms, as it allows to measure, non-invasively and in real-time, the brain activity. However, modeling EEG is still extremely challenging, because of its high-dimensionality, low signal-to-noise ratio, and high individual variability. This paper proposes a novel latent representation to study brain networks using EEG by means of a robust dynamic factor analysis (RDFA) approach. We investigate the ability of this latent representation to discriminate between two groups of subjects, i.e. alcoholic and healthy.

By RDFA, we can extract a limited number of highly explanatory factors, as low as 8, significantly discriminating between the two groups. Also, we show that different brain patterns can be identified across different stimulation scenarios and EEG locations. Although preliminary, this work could give support to domain experts while providing some clinically-meaningful insights to identify common patterns as well as individual characteristics in different groups of healthy and pathological subjects.

脑电图（EEG）是研究正常和病理大脑机制最常用的技术之一，因为它可以无创和实时地测量大脑活动。然而，由于脑电图的高维性、低信噪比和高个体变异性，脑电图建模仍然极具挑战性。本文通过鲁棒动态因子分析（RDFA）方法，提出了一种利用脑电图研究大脑网络的新型潜表征。通过 RDFA，我们可以提取数量有限的高解释性因子（低至 8 个），从而显著区分两组受试者。此外，我们还表明，在不同的刺激场景和脑电图位置下，可以识别出不同的大脑模式。这项工作虽然是初步的，但可以为领域专家提供支持，同时提供一些对临床有意义的见解，以识别不同健康和病理受试者群体的共同模式和个体特征。

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引用次数: 0

On the adaptation of in-context learners for system identification 论适应系统识别的情境学习者

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.08.541

Dario Piga, Filippo Pura, Marco Forgione

In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems. This paradigm facilitates the leveraging of knowledge acquired from observing the behaviour of different, yet related dynamics. This paper discusses the role of meta-model adaptation. Through numerical examples, we demonstrate how meta-model adaptation can enhance predictive performance in three realistic scenarios: tailoring the meta-model to describe a specific system rather than a class; extending the meta-model to capture the behaviour of systems beyond the initial training class; and recalibrating the model for new prediction tasks. Results highlight the effectiveness of meta-model adaptation to achieve a more robust and versatile meta-learning framework for system identification.

上下文系统识别旨在构建元模型来描述系统类别，有别于为单一系统建模的传统方法。这种模式有助于利用从观察不同但相关的动态行为中获得的知识。本文讨论了元模型适应性的作用。通过数字示例，我们展示了元模型适应如何在三种现实场景中提高预测性能：定制元模型以描述特定系统而非类别；扩展元模型以捕捉初始训练类别之外的系统行为；以及针对新的预测任务重新校准模型。研究结果凸显了元模型适应性的有效性，从而为系统识别提供了一个更稳健、用途更广泛的元学习框架。

引用次数: 0

Variational Integrators for Stochastic Mechanical Hybrid Systems 随机机械混合系统的变分积分器

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.08.272

K.C. Tejaswi , Taeyoung Lee

This paper introduces stochastic variational impact integrators for the class of hybrid mechanical systems that incorporate random noise. The governing equations are obtained by the application of the variational principle to the stochastic action integral, where both the continuous-time dynamics as well as the discrete transitions are considered. Furthermore, structure-preserving geometric integrators are derived through the discretization of the stochastic variational principle. This ensures the consistency in comparison to the continuous versions of the Euler-Lagrange or Hamilton’s equations. The effectiveness of the proposed methods in capturing the long-term energy behavior of a stochastic mechanical hybrid system is illustrated by numerical examples.

本文介绍了一类包含随机噪声的混合机械系统的随机变分影响积分器。通过将变分原理应用于随机作用积分，既考虑了连续时间动力学，又考虑了离散转换，从而得到了治理方程。此外，通过随机变分原理的离散化，还推导出了结构保持几何积分器。这确保了与连续版本的欧拉-拉格朗日方程或汉密尔顿方程的一致性。通过数值示例说明了所提出的方法在捕捉随机机械混合系统的长期能量行为方面的有效性。

引用次数: 0

Variational Estimation for Mechanical Systems on Lie Groups based on Geometric Mechanics 基于几何力学的李群上机械系统的变量估算

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.08.302

Amit K. Sanyal

Geometric mechanics analyzes mechanical systems in the framework of variational mechanics, while accounting for the geometry of the configuration space. From the late 1970s, developments in this area produced several schemes for geometric control of mechanical systems in continuous time and discrete time. In the mid to late 2000s, geometric mechanics was first applied to state estimation of mechanical systems, particularly systems evolving on Lie groups as configuration manifolds, like rigid body systems. Much of the existing work on geometric mechanics-based estimation has been in continuous time, using deterministic, semi-stochastic and stochastic approaches. While the body of existing literature on discrete-time estimation schemes on Lie groups is not as extensive, the literature on this topic is contemporaneous with continuous-time schemes. This work describes some recent and ongoing research on geometric mechanics-based estimation schemes in continuous and discrete time from the mid-2010s, which were developed using the Lagrange-d’Alembert principle applied to rigid body systems. This approach gives (deterministic) observer designs with strong stability and robustness properties. This work concludes with potential extensions of this approach to mechanical systems with principal fiber bundles as configuration manifolds.

几何力学在变分力学的框架内分析机械系统，同时考虑配置空间的几何。从 20 世纪 70 年代末开始，这一领域的发展产生了若干连续时间和离散时间机械系统的几何控制方案。在 2000 年代中后期，几何力学首次被应用于机械系统的状态估计，特别是在作为构型流形的李群上演化的系统，如刚体系统。现有的基于几何力学的估算工作大多采用连续时间，使用确定性、半随机和随机方法。虽然有关离散时间李群估算方案的现有文献并不广泛，但有关该主题的文献与连续时间方案是同步的。本著作介绍了自 2010 年代中期以来，基于几何力学的连续和离散时间估计方案的一些最新和正在进行的研究，这些方案是利用应用于刚体系统的拉格朗日-达朗贝尔原理开发的。这种方法提供的（确定性）观测器设计具有很强的稳定性和鲁棒性。这项研究最后提出了将这种方法扩展到以主纤维束为配置流形的机械系统的可能性。

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引用次数: 0

Port-Hamiltonian modeling of a geometrically nonlinear hyperelastic beam⁎ 几何非线性超弹性梁的端口-哈密顿模型⁎

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.08.299

Cristobal Ponce , Yongxin Wu , Yann Le Gorrec , Hector Ramirez

This paper is concerned with the port-Hamiltonian modeling of a Timoshenko beam subject geometric nonlinearities through von Kármán strains, material nonlinearity considering hyperelasticity with the assumption of neo-Hookean or Mooney-Rivlin material, in addition to the incompressible deformation constraint that corresponds to the preservation of volume. The model is suitable for representing the behavior of rubber like beams within the range of moderate deformations and rotations. Numerical simulations are carried out to illustrate the accuracy of the proposed model.

本文关注的是 Timoshenko 梁的端口-哈密顿模型，该梁通过 von Kármán 应变受到几何非线性影响，材料非线性考虑了新胡克或穆尼-里夫林材料假设下的超弹性，此外还有与体积保持一致的不可压缩变形约束。该模型适用于在中等变形和旋转范围内表示类似橡胶梁的行为。为了说明所提议模型的准确性，我们进行了数值模拟。

引用次数: 0

Surrogate Modeling of a Lumped-Mass Multibody Structure Using Hamiltonian Neural Networks 利用哈密顿神经网络建立块体质量多体结构的替代模型

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.08.255

Vitor B. Santos , Flávio Luiz Cardoso-Ribeiro , Andrea Brugnoli

The complexity of highly flexible structures restricts their use in real-time simulations. To address this challenge, we investigate the use of Hamiltonian neural networks (HNNs) as an alternative method for modeling a highly flexible cantilever beam. We derived the reference structural model using a lumped-mass rigid multibody method considering the Hamiltonian formalism and used it to generate a dataset consisting of generalized coordinates and momenta as inputs and their respective time derivatives as outputs. The trained neural networks are used as surrogate models to simulate the cantilever beam under free and forced conditions. Preliminary findings indicate that HNNs create accurate and efficient surrogate models whilst learning conservation laws. For forced-response simulations, our approach requires analytical calculation of external forces, offsetting the computational Efficiency gains of our surrogate models. The outcomes of this study give initial perspectives and limitations of the use of surrogate models based on HNNs as a means to efficient simulations of highly flexible structures.

高柔性结构的复杂性限制了其在实时模拟中的应用。为了应对这一挑战，我们研究了使用哈密顿神经网络（HNN）作为高柔性悬臂梁建模的替代方法。我们使用考虑到哈密顿形式主义的块质量刚性多体方法推导出了参考结构模型，并用它生成了一个数据集，其中包括作为输入的广义坐标和力矩，以及作为输出的它们各自的时间导数。训练好的神经网络被用作模拟自由和受力条件下悬臂梁的代理模型。初步研究结果表明，HNN 在学习守恒定律的同时，还能创建精确高效的代用模型。对于强迫响应模拟，我们的方法需要对外力进行分析计算，这抵消了代用模型在计算效率方面的优势。本研究的成果为使用基于 HNN 的代用模型高效模拟高柔性结构提供了初步的视角和局限性。

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引用次数: 0

Fast simulation of the Kirchhoff-Carrier string with an energy-storing boundary condition using a Scalar Auxiliary Variable approach 使用标量辅助变量方法快速模拟具有储能边界条件的基尔霍夫-开利弦

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.08.284

Michele Ducceschi , Alexis Mousseau , Stefan Bilbao , Riccardo Russo

Various string vibration models exist; linear models are common in musical acoustics but lack accuracy for complex phenomena. Nonlinear terms are necessary for pitch glides and modal couplings at higher amplitudes. Realistic boundary conditions are vital, often overlooked for simplicity. This study proposes an efficient time-stepping routine for nonlinear strings with energy-storing boundaries, derived from the Scalar Auxiliary Variable method, allowing fast inversion using the Sherman-Morisson formula.

弦乐振动模型多种多样；线性模型在音乐声学中很常见，但对复杂现象缺乏准确性。对于较高振幅下的音高滑行和模态耦合，非线性项是必要的。逼真的边界条件至关重要，但往往因为简单而被忽视。本研究针对具有储能边界的非线性弦提出了一种高效的时间步进程序，该程序源于标量辅助变量法，允许使用谢尔曼-莫里森公式进行快速反演。

引用次数: 0

NMPC in Haptic Shared Control Steering:Optimizing Vehicle Motion 触觉共享控制转向中的 NMPC：优化车辆运动

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.09.059

Richard Gao , Tomohiro Nakade , Robert Fuchs , Jürg Schiffmann

As vehicles become more automated in the pursuit of comfort and safety, the human-machine interface must adapt to accommodate the intent of both driver and automation. The concept of haptic shared control steering allows lateral control of the vehicle to be continuously shared between driver and automation via the steering wheel. The automation imparts a torque on the steering wheel which serves to both guide the vehicle and inform the driver of the automation's intent without impairing the driver's ability to steer the vehicle. The combination of haptic feedback and seamless transition of vehicle control makes the use of automation more intuitive. This paper describes a Nonlinear Model Predictive Control (NMPC) approach for controlling the strength of the automation torque with the aim of optimizing vehicle motion. NMPC allows for intuitive tuning and customization of the vehicle steering performance through the NMPC parameterization. Results on a vehicle in real driving scenarios show that the proposed control is able to decrease the lateral jerk, a common measure of passenger comfort, compared to the current state of the art by up to 50% while maintaining the same or similar levels of path tracking performance.

随着车辆的自动化程度越来越高，为了追求舒适性和安全性，人机界面必须适应驾驶员和自动化系统的意图。触觉共享控制转向的概念允许驾驶员和自动驾驶系统通过方向盘持续共享对车辆的横向控制。自动驾驶系统在方向盘上施加扭矩，既能引导车辆，又能将自动驾驶系统的意图告知驾驶员，而不会影响驾驶员操纵车辆的能力。触觉反馈与车辆控制无缝过渡的结合使自动驾驶的使用更加直观。本文介绍了一种用于控制自动化扭矩强度的非线性模型预测控制（NMPC）方法，旨在优化车辆运动。通过 NMPC 参数设置，可以直观地调整和定制车辆转向性能。在实际驾驶场景中对车辆进行测试的结果表明，与目前的技术水平相比，所提出的控制方法能够在保持相同或类似水平的路径跟踪性能的同时，将横向颠簸（衡量乘客舒适度的常用指标）降低多达 50%。

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引用次数: 0

Vehicle motion planning for ride comfort using subjective vertical conflict model 利用主观垂直冲突模型进行车辆运动规划，提高乘坐舒适性

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.09.060

Takumi Todaka , Kaito Sato , Kenji Sawada , Katsuhiko Sando

As the next step regarding vehicle motion planning techniques for self-driving vehicles, controlling the optimal behavior of the passenger is attracting attention. This paper discusses a nonlinear model predictive control (NMPC) method for vehicle motion planning that controls passenger behaviors. In this paper, a nonlinear passenger model is incorporated into NMPC for vehicle motion planning to suppress the motion causing discomfort to the passenger. In addition, ride comfort is evaluated based on the passenger's motion perception characteristics obtained from the subjective vertical conflict model.

作为自动驾驶汽车运动规划技术的下一步，控制乘客的最佳行为正引起人们的关注。本文讨论了一种用于车辆运动规划、控制乘客行为的非线性模型预测控制（NMPC）方法。本文将非线性乘客模型纳入用于车辆运动规划的 NMPC，以抑制引起乘客不适的运动。此外，根据主观垂直冲突模型获得的乘客运动感知特征来评估乘坐舒适度。

引用次数: 0

Time-optimal Point-to-point Motion Planning: A Two-stage Approach 时间最优点对点运动规划：两阶段方法

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2024-01-01 DOI: 10.1016/j.ifacol.2024.09.022

Shuhao Zhang , Jan Swevers

This paper proposes a two-stage approach to formulate the time-optimal point-to-point motion planning problem, involving a first stage with a fixed time grid and a second stage with a variable time grid. The proposed approach brings benefits through its straightforward optimal control problem formulation with a fixed and low number of control steps for manageable computational complexity and the avoidance of interpolation errors associated with time scaling, especially when aiming to reach a distant goal. Additionally, an asynchronous nonlinear model predictive control (NMPC) update scheme is integrated with this two-stage approach to address delayed and fluctuating computation times, facilitating online replanning. The effectiveness of the proposed two-stage approach and NMPC implementation is demonstrated through numerical examples centered on autonomous navigation with collision avoidance.

本文提出了一种两阶段方法来制定时间最优的点到点运动规划问题，其中第一阶段采用固定时间网格，第二阶段采用可变时间网格。所提方法的好处在于，它的最优控制问题表述简单明了，控制步骤数量固定且较少，计算复杂度可控，避免了与时间缩放相关的插值误差，尤其是在目标距离较远的情况下。此外，还将异步非线性模型预测控制（NMPC）更新方案与这种两阶段方法相结合，以解决计算时间延迟和波动的问题，促进在线重新规划。通过以避免碰撞的自主导航为中心的数值示例，证明了所提出的两阶段方法和 NMPC 实现的有效性。

引用次数: 0

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