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6th International conference on data-driven Optimization of Complex systems (DOCS)

作者： Xu, Fan Huang, Jiaxin Tang, Fangyifan Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Jiangxi Peoples R China

ISBN: (纸本)9798350377859;9798350377842

This paper presents a novel method for predicting momentum in competitive settings using machine learning algorithms. The study focuses on integrating various momentum indicators into a comprehensive assessment model. We employ a hierarchical analysis method to determine the weighting coefficients of these indicators. The model includes fundamental momentum, scoring psychology, pressure on the opponent, and offensive momentum, calculated through an exponentially weighted moving average. Additionally, we implement an XGBoost regression model for trend prediction, leveraging detailed match data. The model's effectiveness is evaluated through SHAP analysis, demonstrating its interpretative capabilities and predictive accuracy. Our approach offers a robust framework for momentum prediction, applicable across various competitive domains.

关键词： Momentum prediction EMA Strong positive correlation XGBoost regression

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Meta-Reinforcement-learning-Based Current control of Permanent Magnet Synchronous Motor Drives for a Wide Range of Power Classes

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ieee TRANSACTIONS ON POWER ELECTRONICS 2023年第7期38卷 8062-8074页

作者： Jakobeit, Darius Schenke, Maximilian Wallscheid, Oliver Paderborn Univ Dept Power Elect & Elect Dr D-33098 Paderborn Germany

data-driven reinforcement-learning-based controller schemes have much potential to aid the design of model-free control algorithms that can be trained without the necessity of plant-specific parameter knowledge. Unfortunately, the corresponding training phase is a time-consuming and possibly money-consuming process, which needs to be repeated whenever application to a new plant system is requested. To reduce the total training time for a large set of heterogeneous plant systems, this article proposes a meta-reinforcement-learning-based approach that is to be utilized for the control of hundreds of different permanent magnet synchronous motor drives ranging from a few watts to hundreds of kilowatts. The so-called context variables carry the meta-information about the set of considered drive systems. Their estimation using a corresponding artificial neural network as context approximator is a core aspect of this article. The context information allows the reinforcement-learning-based control algorithm to automatically adapt itself to individual motor drives without requiring individual plant training. Since the found context variables can also be interpreted as an implicit system identification result, they allow us to determine irregular plant behavior (e.g., faulty drives) as an added bonus of the proposed meta-reinforcement learning scheme. Empirical results during this proof of concept successfully validate the potential of the proposed approach to drastically reduce the total training time and encourage further research.

关键词： Artifical neural networks control meta-reinforcement learning (MRL) permanent magnet synchronous motors (PMSMs) system identification

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Adaptive Super-Twisting Sliding Mode control for Friction Compensation of Servo System Via AESO and Parameters Estimation 13

Adaptive Super-Twisting Sliding Mode Control for Friction Co...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Wang, Shun Tao, Liang Deng, Xiongfeng Xu, Binzi Shi, Linlin College of Electrical Engineering Anhui Polytechnic University Wuhu241000 China College of Engineering South China Agricultural University Guangzhou510642 China

ISBN: (纸本)9798350361674

This paper presents a super-twisting sliding mode control strategy utilizing an adaptive extended state observer (AESO) for servo systems with nonlinear friction compensation to tackle the challenges of precise tracking. Firstly, an advanced online parameter identification mechanism is adopted to identify the unknown parameters of the continuous friction model within the system. This mechanism dynamically updates parameters in real-time, effectively converging to the true values of the parameters in response to the dynamic changes of the system. Subsequently, the AESO is employed to accurately estimate and compensate for the lumped disturbances, encompassing external disturbances and parameter estimation errors. Then, an adaptive super-twisting sliding mode controller is proposed. The controller is meticulously engineered to ensure the system's state accurately adheres to a predefined reference trajectory, effectively reducing errors stemming from model uncertainties and the influence of external disturbances. To empirically substantiate the efficacy of the proposed methods, a comprehensive series of simulation experiments are conducted, showcasing the superiority of the proposed method in terms of tracking accuracy and system stability. © 2024 ieee.

关键词： Sliding mode control

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Fault-Tolerant control for Electromagnetic Suspension System of Maglev Train: Comprehensive Research and Design 13

Fault-Tolerant Control for Electromagnetic Suspension System...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Junhao Dai, Chunhui Sun, Shangrongxi Wang, Hao Li, Xiaolong College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha410073 China Hunan Provincial Key Laboratory of Electromagnetic Levitation and Propulsion Technology Hunan Changsha410073 China Northwest Institute of Electromechanical Technology Shanxi Xian712099 China

ISBN: (纸本)9798350361674

Maglev technology, as an advanced transit technology, have garnered global attention due to their remarkable high-speed performance, energy efficiency, environmental friendliness, and comfortable ride experience. They have been actively promoted and put into practical operation in numerous countries and regions worldwide. Given the complexity of magnetic levitation train systems and their sensitivity to external disturbances, enhancing their reliability and robust stability is of paramount importance. This paper takes the conventional electromagnetic suspension (EMS) maglev train developed by the National University of Defense Technology as a reference model, and systematically investigates the fault-tolerant control techniques for magnetic levitation trains based on accurate sensor fault detection and localization. The study encompasses the implementation of active fault tolerance through dynamic switching control strategies and sensor failures, as well as simultaneous passive fault-tolerant control schemes that exploit the inherent redundancy characteristics of the joining-structure within the magnetic levitation train system. Furthermore, the effectiveness and practicability of these fault-tolerant control methods were validated using a Digital Signal Processor (DSP) digital controller. The experimental results demonstrated that the summarized fault-tolerant control system meets the predefined performance criteria, thereby substantiating the validity and practicality of the relevant fault-tolerance control technologies. © 2024 ieee.

关键词： Magnetic levitation

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iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative learning

iMTSP: Solving Min-Max Multiple Traveling Salesman Problem w...

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2024 International conference on Intelligent Robots and systems

作者： Guo, Yifan Ren, Zhongqiang Wang, Chen Purdue Univ Flight Dynam & Control Hybrid Syst Lab W Lafayette IN 47907 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Buffalo Dept Comp Sci & Engn Spatial AI & Robot SAIR Lab Buffalo NY 14260 USA

ISBN: (纸本)9798350377712;9798350377705

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20x faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents).

关键词： Traveling salesman problem

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A Comprehensive Machine learning Methodology for Embedded systems PHM 22

A Comprehensive Machine Learning Methodology for Embedded Sy...

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ieee 22nd International conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Pimentel, Juliano McEwan, Alistair A. Yu, Hong Qing Univ Derby Sch Comp & Engn Derby DE22 1GB England

ISBN: (纸本)9798350381993;9798350382006

Techniques of Machine learning (ML) have recently been widely used in several applications, but not much for embedded systems, particularly those of safety-critical functionality, and are expected to help solve complex PHM and RUL which are difficult to be overcome with traditional approaches. This paper proposes a novel approach to encompass data-driven system-level health management, which comprises three main processes: an exhaustive offline training using machine learning techniques to derive a model capable of predicting the system of interest within fixed performance criteria;followed by its deployment on the targeted embedded system for real-time health assessment and finally the ability to provide a multi-step ahead forecasting for the system prognostics. The ability to monitor the current state of the systems and predict their behaviour becomes essential. Therefore, techniques to establish fault tolerance and fault prediction are required. Condition monitoring (CM), prognostics and health management (PHM) and remaining useful life (RUL) are enablers for fault detection, fault progression and system degradation. This paper presents a ML-based methodology for the PHM of embedded systems, aiming at generalization and real-time performance.

关键词： PHM Machine learning Embedded systems Real-Time Condition Monitoring Remaining Useful Life Fault Taxonomy

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data-driven control of nonlinear systems from input-output data 62

Data-driven control of nonlinear systems from input-output d...

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62nd ieee conference on Decision and control (CDC)

作者： Dai, X. De Persis, C. Monshizadeh, N. Tesi, P. Univ Groningen Inst Engn & Technol Groningen NL-9747 AG Groningen Netherlands Univ Florence Dept Informat Engn I-50139 Florence Italy

ISBN: (纸本)9798350301243

The design of controllers from data for nonlinear systems is a challenging problem. In a recent paper, De Persis, Rotulo and Tesi, "learning controllers from data via approximate nonlinearity cancellation," ieee Transactions on Automatic control, 2023, a method to learn controllers that make the closed-loop system stable and dominantly linear was proposed. The approach leads to a simple solution based on data-dependent semidefinite programs. The method uses input-state measurements as data, while in a realistic setup it is more likely that only input-output measurements are available. In this note we report how the design principle of the above mentioned paper can be adjusted to deal with input-output data and obtain dynamic output feedback controllers in a favourable setting.

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learning-Aided Kalman Tracking in Biased Dynamic systems: The Case of Cable-driven Robots for Surgery

Learning-Aided Kalman Tracking in Biased Dynamic Systems: Th...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ketashvili, Linoy Ashkenasy, Shachar Nisky, Ilana Shlezinger, Nir School of ECE Ben-Gurion University of the Negev Beer-Sheva Israel Department of BME Ben-Gurion University of the Negev Israel

ISBN: (纸本)9798350368741

In cable-driven robots, the actuation is transmitted via long cables for control and manipulation of the end-effectors. The cables introduce biases due to non-linear tension, creating a challenge for accurate modeling and localization. This paper presents a novel data-driven algorithm that addresses these biases in partially known dynamic systems, focusing on cable-driven robots. The proposed approach integrates classic Kalman filtering with deep learning to enhance tracking and overcome the limitations of existing state-space modeling. Our algorithm learns from data to explicitly track the robot's end-effector, while implicitly tracking its cable-induced bias. We demonstrate the effectiveness of the algorithm on two different robotic manipulators: a planar two-link robotic manipulator, and the Raven surgical robot, constituting testbeds for dynamic systems where the bias arises from time-varying cable tension. Our experimental results reveal that while conventional methods like the Extended Kalman Filter struggle with pose tracking due to system biases, our algorithm successfully localizes the robot's end-effector and uncovers underlying biases, showing significant advancements in predictive capabilities and dynamic system understanding. © 2025 ieee.

关键词： Cable-driven robots Kalman filter

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SG-DLCT: Saliency-Guided Dual-Level Collaborative Transformer for Image Captioning 13

SG-DLCT: Saliency-Guided Dual-Level Collaborative Transforme...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhu, Xinchao Deng, Jingzheng Zhang, Kanjian Zhang, Jinxia Southeast University Shenzhen Research Institute Shenzhen518057 China School of Automation Southeast University The Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350361674

Improving the generation of feature representations for images has been a central focus in the field of image captioning. However, existing approaches, whether based on grid features or region features, only consider the features within individual regions without capturing the interactions between objects. In this paper, we propose a novel Saliency-Guided Dual-Level Collaborative Transformer (SG-DLCT), which leverages saliency information to guide the network in capturing the importance of different objects and their interrelationships. Concretely, we utilize a pre-trained RGBD salient object ranking model to obtain saliency information. Building upon this, we propose the Saliency Feature Enhanced Module (SFEM), which optimizes region features using saliency values as weights, thereby guiding the model to focus on image regions that are more meaningful for generating image captions. Additionally, we propose the Saliency-Guided Cross Attention (SGCA), which leverages saliency information to model the interaction between region features and grid features, allowing the final model's features to benefit from the advantages of both types of features. Experimental results validate the significance of saliency information in image captioning tasks and demonstrate the effectiveness of our proposed method. © 2024 ieee.

关键词： Image Captioning Salient Object Ranking Transformer

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Graph-Structured Super-Resolution for Geometry- Generalized Tomographic Tactile Sensing: Application to Humanoid Faces

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ieee TRANSACTIONS ON ROBOTICS 2025年 41卷 558-572页

作者： Park, Hyunkyu Kim, Woojong Jeon, Sangha Na, Youngjin Kim, Jung Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Sookmyung Womens Univ Dept Mech Syst Engn Seoul 04310 South Korea

Electrical impedance tomographic (EIT) tactile sensing holds great promise for whole-body coverage of contact-rich robotic systems, offering extensive flexibility in sensor geometry. However, low spatial resolution restricts its practical use, despite the existing deep-learning-based reconstruction methods. This study introduces EIT-GNN, a graph-structured data-driven EIT reconstruction framework that achieves super-resolution in large-area tactile perception on unbounded form factors of robots. EIT-GNN represents the arbitrary sensor shape into mesh connections, then employs a twofold architecture of transformer encoder and graph convolutional neural network to best manage such the geometrical prior knowledge, resulting in the accurate, generalized, and parameter-efficient reconstruction procedure. As a proof-of-concept, we demonstrate its application using large-area face-shaped sensor hardware, which represents one of the most complex geometries in human/humanoid anatomy. An extensive set of experiments, including simulation study, ablation analysis, single-touch indentation test, and latent feature analysis, confirm its superiority over alternative models. The beneficial features of the approach are demonstrated through its application in active tactile-servo control of humanoid head motion, paving the new way for integrating tactile sensors with intricate designs into robotic systems.

关键词： Geometry Sensors Tactile sensors Image reconstruction Electrical impedance tomography Robots Shape Hardware Faces Calibration Deep learning in robotics and automation force and tactile sensing sensor-based control tomographic reconstruction

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