检索结果-内蒙古大学图书馆

4th modeling, Estimation, and Control Conference (MECC)

作者： Echeveste, Salvador Mondal, Md Safwan Ramasay, Subramanian Bhounsule, Pranav A. Univ Illinois Dept Mech & Ind Engn 842 W Taylor St Chicago IL 60607 USA

Hip exoskeletons offer significant potential for enhancing human movement, especially for those with mobility impairments. However, optimizing their performance typically involves lengthy discrete and continuous optimization methods. To address this, we propose a novel approach using machine learning to predict controller parameter classes, aiming to improve the tuning process. Our method relies on subject-specific anthropometric data to predict optimal controller parameters for hip exoskeletons. Through a machine learning framework, we develop predictive models to determine the most effective parameter settings tailored to individual users. By employing feature engineering, data synthesis techniques, and model training, we enhance the initialization of Bayesian Human-in-the-loop (HIL) optimization. Results indicate that our machine learning models can predict control parameter classes with 75% accuracy, leading to a 9.98% improvement in optimized exoskeleton performance for users.

关键词： Assistive and Rehabilitation Robotics Optimal Control machine learning in modeling estimation and control

来源：评论

学校读者我要写书评

暂无评论

learning dissipative Hamiltonian dynamics with reproducing kernel Hilbert spaces and random Fourier features 4

Learning dissipative Hamiltonian dynamics with reproducing k...

引用

4th modeling, Estimation, and Control Conference (MECC)

作者： Smith, Torbjorn Egeland, Olav Norwegian Univ Sci & Technol NTNU Dept Mech & Ind Engn NO-7491 Trondheim Norway

This paper presents a new method for learning dissipative Hamiltonian dynamics from a limited and noisy dataset. The method uses the Helmholtz decomposition to learn a vector field as the sum of a symplectic and a dissipative vector field. The two vector fields are learned using two reproducing kernel Hilbert spaces, defined by a symplectic and a curl-free kernel, where the kernels are specialized to enforce odd symmetry. Random Fourier features are used to approximate the kernels to reduce the dimension of the optimization problem. The performance of the method is validated in simulations for two dissipative Hamiltonian systems, and it is shown that the method improves predictive accuracy significantly compared to a method where a Gaussian separable kernel is used. Copyright (c) 2024 The Authors.

关键词： machine learning in modeling estimation control

来源：评论

学校读者我要写书评

暂无评论

Geometric Graph Neural Network modeling of Human Interactions in Crowded Environments 4

Geometric Graph Neural Network Modeling of Human Interaction...

引用

4th modeling, Estimation, and Control Conference (MECC)

作者： Honarvar, Sara Diaz-Mercado, Yancy Univ Maryland Dept Mech Engn College Pk MD 20742 USA

modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using complete graphs, we define interaction neighborhoods using pedestrians' field of view, motion direction, and distance-based kernel functions to construct graph representations of crowds. Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics. Our findings underscore the importance of integrating domain knowledge with data-driven approaches for effective modeling of human interactions in crowds.

关键词： machine learning in modeling estimation and control modeling and Validation Multi-agent and Networked Systems Graph Neural Network Crowd Navigation

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Estimation of Region of Attraction Using Koopman Operator and Reverse Trajectory 4

Data-Driven Estimation of Region of Attraction Using Koopman...

引用

4th modeling, Estimation, and Control Conference (MECC)

作者： Velasco, Rober Boker, Almuatazbellah Mili, Lamine Abolmasoumi, Amir Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA Arak Univ Dept Elect Engn Arak Iran

We propose to estimate the region of attraction (ROA) for the stability of nonlinear systems from only system measurement data and without knowledge of the system model. The key to our result is the use of Koopman operator theory to approximate the nonlinear dynamics in linear coordinates. This approximation is typically more accurate than the traditional Jacobian-based linearization method. We then employ the Extended Dynamic Mode Decomposition (EDMD) method to estimate the linear approximation of the system through data. This is then used to construct a Lyapunov function that helps estimate the ROA. However, this estimate is typically very conservative. The trajectory reversing method is then used on the set of points that form this conservative estimate, to enlarge the ROA approximation. The output of EDMD is also utilized in the trajectory reversing method, keeping the entire analysis data-driven. Finally, an example is used to show the accuracy of this data-driven method, despite not knowing the system. Copyright (c) 2024 The Authors.

关键词： Region of Attraction Koopman Operator Theory Stability of Nonlinear Systems machine learning in modeling Estimation Control

来源：评论

学校读者我要写书评

暂无评论

Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems 4

Estimation Sample Complexity of a Class of Nonlinear Continu...

引用

4th modeling, Estimation, and Control Conference (MECC)

作者： Kuang, Simon Lin, Xinfan Univ Calif Davis Davis CA 95616 USA

We present a method of parameter estimation for large class of nonlinear systems, namely those in which the state consists of output derivatives and the flow is linear in the parameter. The method, which solves for the unknown parameter by directly inverting the dynamics using regularized linear regression, is based on new design and analysis ideas for differentiation filtering and regularized least squares. Combined in series, they yield a novel finite-sample bound on mean absolute error of estimation.

关键词： Modelling Identification and Signal Processing machine learning in modeling estimation and control Estimation

来源：评论

学校读者我要写书评

暂无评论

machine-learning based Tire-Road Friction Prediction for Ground Vehicles 2nd

Machine-Learning based Tire-Road Friction Prediction for Gro...

引用

2nd modeling, Estimation and Control Conference (MECC)

作者： Scott, Perry Wang, Junmin Univ Texas Austin Walker Dept Mech Engn Austin TX 78712 USA

The presence of adverse road conditions like water, snow, and ice is known to largely increase the chances of road vehicle crashes due to reduced vehicle performance. Providing useful information about future tire-road friction characteristics that will influence vehicle traction and braking capability are essential for safe operation under such weather conditions. This paper describes an approach for using a low-cost camera, vehicle GPS and other basic vehicle sensors on a scaled autonomous vehicle to build a Neural Network for slip-slope prediction. A Recursive Least Squares Filter is used to estimate the longitudinal friction slip-slope. As an alternative to surface classification which requires a labelled data set, this method uses an easily obtained slip-slope to infer tire-road friction levels. A preliminary experiment is conducted which indicates the neural networks effectiveness to distinguish slip-slope values based on previewed road images captured by the vehicle camera. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： machine learning in modeling estimation and control Estimation

来源：评论

学校读者我要写书评

暂无评论

learning dissipative Hamiltonian dynamics with reproducing kernel Hilbert spaces and random Fourier features

引用

IFAC-PapersOnLine 2024年第28期58卷 1115-1120页

作者： Torbjørn Smith Olav Egeland Department of Mechanical and Industrial Engineering Norwegian University of Science and Technology (NTNU) NO-7491 Trondheim Norway

关键词： machine learning in modeling estimation control

来源：评论

学校读者我要写书评

暂无评论

Enhancing Hip Exoskeleton Tuning Performance with machine learning: An Anthropometric Data-Driven Approach

引用

IFAC-PapersOnLine 2024年第28期58卷 43-48页

作者： Salvador Echeveste Md. Safwan Mondal Subramanian Ramasay Pranav A. Bhounsule Department of Mechanical and Industrial Engineering University of Illinois at Chicago 842 W Taylor St. Chicago IL 60607 USA

关键词： Assistive Rehabilitation Robotics Optimal Control machine learning in modeling estimation control

来源：评论

学校读者我要写书评

暂无评论

Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems ⁎

引用

IFAC-PapersOnLine 2024年第28期58卷 786-791页

作者： Simon Kuang Xinfan Lin University of California Davis； Davis CA 95616

关键词： Modelling Identification Signal Processing machine learning in modeling estimation control Estimation

来源：评论

学校读者我要写书评

暂无评论

Digital Twin Design and Cross Process Model Transfer for Additive Manufacturing ⁎

引用

IFAC-PapersOnLine 2024年第28期58卷 875-880页

作者： Frank Y. Lai Eric Shen James Fonda Al Salour Dawn Tilbury Kira Barton University of Michigan Ann Arbor MI 48109 USA Boeing Additive Manufacturing Intelligence Center

Digital twins serve as powerful tools for monitoring the status and anticipating errors within manufacturing processes. However, the creation of these digital twins entails a significant investment in time and resources. This paper delves into the process of crafting digital twins for material extrusion additive manufacturing. Furthermore, it investigates the application of cross model transfer learning to such digital twins, and assesses the feasibility in transferring digital twins rather than building them from the ground up.

关键词： Modelling Identification Signal Processing machine learning in modeling estimation control Manufacturing Systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：