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14th Asian control conference (ASCC)

作者： Liu, Genfeng Wang, Yangyang Hou, Zhongsheng Henan Univ Technol Coll Elect Engn Zhengzhou Peoples R China Informat Engn Univ PLA Strateg Support Force Zhengzhou Peoples R China Qingdao Univ Sch Automat Qingdao Peoples R China

ISBN: (纸本)9798331540845;9789887581598

In this paper, a model-free adaptive predictive quantitative control algorithm is presented for unknown nonlinear systems to handle the limited network transmission capacity and false data injection attacks. Firstly, a uniform quantizer with encoding-decoding mechanism is designed to deal with the network bandwidth limitation and to reduce the effects of quantization errors. Secondly, an attack compensation mechanism is conducted to reduce the impact of the false data injection attacks. Finally, simulation results show the effectiveness and robustness of the proposed control algorithm.

关键词： data-driven control model-free adaptive predictive control MFAC false data injection attacks

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data-driven Forward Stochastic Reachability Analysis for Human-in-the-Loop systems 62

Data-driven Forward Stochastic Reachability Analysis for Hum...

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62nd ieee conference on Decision and control (CDC)

作者： Choi, Joonwon Byeon, Sooyung Hwang, Inseok Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA

ISBN: (纸本)9798350301243

We propose a data-driven forward stochastic reachability analysis algorithm for Human-In-The-Loop (HITL) systems. We focus on a certain type of HITL system whose behavior is dominated by a human operator, for example, a multi-rotor controlled by a human operator. In such a system, the intervention of the human operator may generate a conservative reachable set due to the unpredictable control strategy of the human operator. The proposed algorithm computes a less conservative reachable set of the HITL system by accounting for the human operator's behavior, i.e., we present the data-driven reachability analysis algorithm that considers the unknown controller information of the HITL system. The behavior of the human operator is trained as a Gaussian Mixture Model (GMM) from the state and input trajectories of the system. Then, the conditional probability distribution of the human operator's behavior is obtained from the Gaussian Mixture Regression (GMR) for the closed-loop reachability analysis. The proposed algorithm is tested and demonstrated by the data collected from human subject experiments.

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data-driven Approach for Accurate Wheel Slip Ratio Estimation in Electric Vehicles: Performance Comparison and Validation Analysis 27

Data-Driven Approach for Accurate Wheel Slip Ratio Estimatio...

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27th ieee International conference on Intelligent Transportation systems, ITSC 2024

作者： Ikram, Mubasher Liu, Li Shen, Qiguang Zhang, Qingguang Liu, Jia Xu, Kun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China University of Chinese Academy of Sciences Beijing China Changsha University of Science and Technology Hunan China School of Automotive Technology Liuzhou Railway Vocational and Technical College Liuzhou China

ISBN: (纸本)9798331505929

data-driven techniques advance accurate vehicle state estimation for electric vehicles (EVs), especially for distributed drive electric vehicles (DDEVs) that require precise slip ratio estimation for safe motion control. This study proposes advanced data-driven estimation models of wheel slip ratio using machine learning (ML) techniques, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and extreme learning machine (ELM), to manage the dynamic complexities of EVs effectively. The proposed models process measurable input parameters and are trained on a comprehensive dataset comprising both synthetic and real-vehicle datasets. Comparative and sensitivity analyses evaluate each model's accuracy, robustness, and generalization capabilities. The results reveal highly accurate slip ratio estimation across diverse driving conditions. The LSTM and GRU models demonstrate exceptional performance even under low friction conditions, achieving variance accounted for (VAF) scores of 99% and 98% respectively. Meanwhile, the ELM model proves effective on high adhesion surfaces with an average VAF around 95%. Validation results on real-world data show the GRU model outperforms the others, achieving a VAF of 90%. This study advances vehicle state estimation by offering a robust, data-driven approach incorporating comparative and validation analyses. © 2024 ieee.

关键词： Vehicle wheels

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Spatial-Temporal Dynamic Graph Convolutional Network With Interactive learning for Traffic Forecasting

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年第7期25卷 7645-7660页

作者： Liu, Aoyu Zhang, Yaying Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 201804 Peoples R China

Accurate traffic forecasting is essential in urban traffic management, route planning, and flow detection. Recent advances in spatial-temporal models have markedly improved the modeling of intricate spatial-temporal correlations for traffic forecasting. Unfortunately, most previous studies have encountered challenges in effectively modeling spatial-temporal correlations across various perceptual perspectives and have neglected the interactive learning between spatial and temporal correlations. Additionally, constrained by spatial heterogeneity, most studies fail to consider distinct spatial-temporal patterns of each node. To overcome these limitations, we propose a Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) for traffic forecasting. Specifically, we propose an interactive learning framework composed of spatial and temporal modules for downsampling traffic data. This framework aims to capture spatial and temporal correlations by adopting a perception perspective from the global to the local level and facilitating their mutual utilization with positive feedback. In the spatial module, we design a dynamic graph convolutional network based on graph construction methods. The network is designed to leverage a traffic pattern bank considering spatial-temporal heterogeneity as a query to reconstruct a data-driven dynamic graph structure. The reconstructed graph structure can reveal dynamic associations between nodes in the traffic network. Extensive experiments on eight real-world traffic datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline while balancing computational costs. The source codes are available at https://***/LiuAoyu1998/STIDGCN.

关键词： Correlation Forecasting Roads Convolutional neural networks Traffic control Market research Adaptation models Interactive learning dynamic graph construction graph convolutional network traffic forecasting

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An Active learning-Based Approach for Hosting Capacity Analysis in Distribution systems

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ieee TRANSACTIONS ON SMART GRID 2024年第1期15卷 617-626页

作者： Lee, Kiyeob Zhao, Peng Bhattacharya, Anirban Mallick, Bani K. Xie, Le Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA

With the increasing amount of distributed energy resources (DERs) integration, there is a significant need to model and analyze hosting capacity (HC) for future electric distribution grids. Hosting capacity analysis (HCA) examines the amount of DERs that can be safely integrated into the grid and is a challenging task in full generality because there are many possible integration of DERs in foresight. That is, there are numerous extreme points between feasible and infeasible sets. Moreover, HC depends on multiple factors such as (a) adoption patterns of DERs that depend on socioeconomic behaviors and (b) how DERs are controlled and managed. These two factors are intrinsic to the problem space because not all integration of DERs may be centrally planned, and could largely change our understanding about HC. This paper addresses the research gap by capturing the two factors (a) and (b) in HCA and by identifying a few most insightful HC scenarios at the cost of domain knowledge. We propose a data-driven HCA framework and introduce active learning in HCA to effectively explore scenarios. Active learning in HCA and characteristics of HC with respect to the two factors (a) and (b) are illustrated in a 3-bus example. Next, detailed large-scale studies are proposed to understand the significance of (a) and (b). Our findings suggest that HC and its interpretations significantly change subject to the two factors (a) and (b).

关键词： Voltage Standards Renewable energy sources Load modeling Large scale integration Companies Voltage control Hosting capacity analysis hosting capacity active learning distributed energy resources

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LF²SLAM: learning-based Features For visual SLAM

LF<SUP>2</SUP>SLAM: Learning-based Features For visual SLAM

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2024 International conference on Intelligent Robots and systems

作者： Legittimo, Marco Crocetti, Francesco Fravolini, Mario Luca Mollica, Giuseppe Costante, Gabriele Univ Perugia Dept Engn I-06125 Perugia Italy ART Spa Voc Pischiello 20 I-06065 Passignano Sul Trasimeno Italy

ISBN: (纸本)9798350377712;9798350377705

Autonomous robot navigation relies on the robot's ability to understand its environment for localization, typically using a Visual Simultaneous Localization And Mapping (SLAM) algorithm that processes image sequences. While state-of-the-art methods have shown remarkable performance, they still have limitations. Geometric VO algorithms that leverage hand-crafted feature extractors require careful hyperparameter tuning. Conversely, end-to-end data-driven VO algorithms suffer from limited generalization capabilities and require large datasets for their proper optimizations. Recently, promising results have been shown by hybrid approaches that integrate robust data-driven feature extraction with the geometric estimation pipeline. In this work, we follow these intuitions and propose a hybrid VO method, namely Learned Features For SLAM (LF(2)SLAM), that combines a deep neural network for feature extraction with a standard VO pipeline. The network is trained in a data-driven framework that includes a pose estimation component to learn feature extractors that are tailored for VO tasks. A novel loss function modification is introduced, using a binary mask that considers only the informative features. The experimental evaluation performed shows that our approach has remarkable generalization capabilities in scenarios that differ from those used for training. Furthermore, LF(2)SLAM exhibits robustness in more challenging scenarios, i.e., characterized by the presence of poor lighting and low amount of texture, with respect to the state-of-the-art ORB-SLAM3 algorithm.

关键词： Deep neural networks

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A data-driven Environmental Sound Classification System with Acoustic Triboelectric Nanogenerators

A Data-Driven Environmental Sound Classification System with...

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ieee Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Bagheri, Majid Haji Li, Junyou Gu, Emma Habashy, Karim Rana, Md Masud Khan, Asif Abdullah Zhang, Yanguang Xiao, Gaozhi Xi, Pengcheng Ban, Dayan Univ Waterloo Elect & Comp Engn Waterloo ON Canada Natl Res Council Canada Adv Elect & Photon Ottawa ON Canada Univ Waterloo Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9798350371635;9798350371628

The availability of big data has resulted in significant advancements in deep learning, which have led to increased performance, surpassing human capabilities in tasks, for example, audio classification. Moreover, there is a growing need for energy-efficient, self-sustained sensors that can be massively deployed for managing sound events with intelligence. Acoustic Triboelectric Nanogenerators (TENG) show promise in this context, as they are capable of converting mechanical motion from acoustic waves into electrical signals while being cheap to manufacture. However, leveraging TENGs for environmental sound classification systems requires addressing the challenges associated with data collection and model training. This paper presents a design for an environmental sound classification system that utilizes acoustic TENGs and transformer-based models. To achieve this, we design and fabricate a device composed of cascading TENGs and use the device to record the ESC-50 dataset, which is then used to fine-tune transformer-based models for audio classification. A substantial improvement on model performance (by 44%) has been observed compared to that from a model pre-trained on the original ESC-50 dataset. The results provide valuable insights into the quality of TENG recorded audio, serving as a benchmark for future research in building data-driven environmental sound monitoring systems.

关键词： Audio processing big data triboelectric nanogenerators self-powered sensors microphones deep learning transformers fine-tuning

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data-driven control of input saturated systems: a LMI-based approach 20

Data-driven control of input saturated systems: a LMI-based ...

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20th IFAC Symposium on System Identification (SYSID)

作者： Porcari, F. Breschi, V. Zaccarian, L. Formentin, S. Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Univ Toulouse LAAS CNRS Toulouse France Univ Trento Dipartimento Ingn Ind Trento Italy

This paper addresses three complex control challenges related to input-saturated systems from a data-driven perspective. Unlike the traditional two-stage process involving system identification and model-based control, the proposed approach eliminates the need for an explicit model description. The method combines data-based closed-loop representations, Lyapunov theory, instrumental variables, and a generalized sector condition to formulate data-driven linear matrix inequalities (LMIs). These LMIs are applied to maximize the origin's basin of attraction, minimize the closed-loop reachable set with bounded disturbances, and introduce a new data-driven l(2)-gain minimization problem. Demonstrations on benchmark examples highlight the advantages and limitations of the proposed approach compared to an explicit identification of the system, emphasizing notable benefits in handling nonlinear dynamics. Copyright (C) 2024 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： data-driven control saturated systems linear matrix inequalities

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data-driven Customisable control Design for an Adaptive Cruise control System

Data-driven Customisable Control Design for an Adaptive Crui...

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2025 Australian and New Zealand control conference, ANZCC 2025

作者： Gregory, Jack Rokonuzzaman, Mohammad Mohajer, Navid Watson, Matthew Institute for Intelligent System Research and Innovation Deakin University Australia

ISBN: (纸本)9798331532192

This study proposes a data-driven controller design for Adaptive Cruise control (ACC) systems. The design employs Long Short-Term Memory (LSTM) networks to enable dynamic customisation and superior performance over classical controllers like PID. The proposed LSTM-based controller leverages driving data to optimise control actions, allowing for adaptive and flexible responses in various traffic conditions. The training data for the LSTM network includes the data captured from a desktop motion simulator integrated with MATLAB’s vehicle dynamic block set simulating the motion of the ego vehicle. The results show that the LSTM controller effectively maintains safe following distances and smooth velocity transitions, outperforming traditional methods in terms of adaptability and response time. The LSTM-based controller consistently delivers more stable control actions, particularly in scenarios involving significant velocity variations. These findings highlight the potential of data-driven approaches in enhancing the customisation, efficiency, and safety of ACC systems in real-world applications. © 2025 ieee.

关键词： Adaptive cruise control

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data-driven control for multi-rate sampling singularly perturbed systems 39

Data-driven control for multi-rate sampling singularly pertu...

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39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： He, Yan Zhu, Defu Liu, Peng Sun, Junwei Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This paper investigates the stabilization problem of singularly perturbed hybrid systems with multi-rate sampling. Firstly, we established a hybrid model of singularly perturbed system by designing a multi-rate sampling controller. Secondly, sufficient conditions for global exponential stability of the considered systems are provided using the Lyapunov method. Thirdly, the bounds on the maximum allowable sampling intervals are estimated, and the influence of singular perturbation parameters on the sampling intervals and sampling rates is analyzed. Finally, the effectiveness of the proposed method is verified by the simulation of DC motor model.

关键词： hybrid systems stability analysis data-driven multi-rate sampling

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