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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Msaddi, Saleh Kumbasar, Tufan Control and Automation Engineering Department Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031397769

Accurately assessing uncertainty and prediction of a regression model is essential for making informed decisions, especially in high-risk tasks. Conformal Prediction (CP) is a powerful distribution-free uncertainty quantification framework for building such models as it is capable to transform a single-point prediction of any machine learning model into a Prediction Interval (PI) with a guarantee of encompassing the true value for specified levels of confidence. On the other hand, to generate high-quality PIs, the PIs should be as narrow as possible while enveloping a certain amount of uncertainty (i.e. confidence level). The generated width of the PIs mainly depends on the nonconformity measure used within the CP. In this study, we propose two novel Fuzzy c-Means Clustering (FCM) based nonconformity measures for CP with nearest neighbors to learn distribution-free and high-quality PIs for regression. The proposed approach generates tight PIs by evaluating the degree of nonconformity of a new data point compared to the so-called calibration points via Fuzzy Sets (FSs). From the calibration dataset, we extract representative FSs via FCM and assign every test point alongside the nearest neighbors within the calibration dataset with membership grades to adapt the nonconformity measure. To evaluate the performance, we present statistical comparisons and demonstrate that the proposed FCM-based nonconformity measures result in high-quality PIs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Calibration

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Sliding mode active disturbance rejection control for uncalibrated visual servoing

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International Journal of Systems, control and Communications 2025年第2期16卷 133-151页

作者： Pu, Cuiping Wu, Yi Ren, Jie Ran, Chenyang College of Mechanical and Electrical Engineering Kunming University Kunming China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Department of Automation Shanghai Jiao Tong University Shanghai China

A sliding mode control algorithm based on a linear extended state observer is proposed to address the multi-source uncertainty of uncalibrated visual servoing in robotic arms. Uncertainty, nonlinearity, coupling, external disturbances, etc. are considered as total disturbances. The linear expansion state observer is designed to observe the total disturbance and compensate for it. This enhances the system’s anti-interference ability. The outer loop adopts sliding mode control law as state feedback. It suppresses the observation errors of the observer and ensures the stability of the system. Sliding mode control based on linear extended observer does not depend on specific tasks and system configurations. It provides a unified design framework for solving the problem of uncalibrated visual servoing. Through numerical simulations of three different disturbance cases, it was compared with PI linear extended state observer and linear active disturbance rejection to verify its good dynamic performance and disturbance rejection performance. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Robotic arms

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Extended Kalman Filter Based Nonlinear Probabilistic Model Predictive control of an Autonomous Motorcycle 11

Extended Kalman Filter Based Nonlinear Probabilistic Model P...

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11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Çerçi, Nurettin Yalçin, Yaprak Istanbul Technical University Mechatronics Engineering Department Istanbul Turkey Istanbul Technical University Control and Automation Engineering Department Istanbul Turkey

ISBN: (纸本)9798350362541

This work focuses on the control of a nonlinear autonomous motorcycle model using Probabilistic Model Predictive control (MPC). The main purpose of this work is to control the speed, yaw angle and roll angle of the motorcycle while keeping its balance during a maneuver. The Probabilistic MPC takes into account the probabilistic disturbances acting on the control inputs or the state variables. The cost function of the optimization of this proposed nonlinear probabilistic MPC is different than the cost of traditional deterministic MPC, such that, it contains terms that represent the mean of the state variable which is calculated with the help of an Extended Kalman Filter (EKF) and also the terms as the functions of covariances of the state variables and control inputs. The optimization algorithm is fed with these mean and covariance values every time instant, then the controller calculates the optimal control sequence to apply it to the system model. The speed of the motorcycle, the yaw angle and, the roll angle of the motorcycle are controlled with good efficiency. © 2024 IEEE.

关键词： Extended Kalman filters

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Enhancing Visual Odometry Accuracy through Block-Based Techniques

Enhancing Visual Odometry Accuracy through Block-Based Techn...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Atilgan, Cem Mercimek, Muharrem Department of Mechatronics Engineering Kırklareli University Kirklareli Turkey Department of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9798350379433

Visual odometry (VO) is a method used to estimate the spatial movement of vehicles or camera-equipped systems by analyzing visual data from the environment. It is a cost-effective and accurate alternative to systems like Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS), and is particularly favored in autonomous vehicles, unmanned aerial vehicles (UAVs), and augmented reality applications. This study focuses on the performance challenges faced by visual odometry systems in low-light environments, where traditional systems may not be effective and presents a novel approach, Contrast-Adaptive Block Optimization (CABO), to enhance VO in low-light environments. CABO divides images into blocks and selectively applies Contrast Limited Adaptive Histogram Equalization (CLAHE) to darker regions, optimizing image quality without compromising natural appearance. By improving feature extraction and matching, CABO enhances VO accuracy and robustness. We utilized the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset, we demonstrate the effectiveness of the proposed method in improving position and orientation estimation under challenging lighting conditions. © 2024 IEEE.

关键词： Inertial navigation systems

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Wavelet Transformation for Recognizing Various Defects in Diagnosis of Power Transformers

Wavelet Transformation for Recognizing Various Defects in Di...

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2024 International Russian automation Conference, RusAutoCon 2024

作者： Kuznetsov, Andrey A. Volchanina, Maria A. Gorlov, Anton V. Department 'Theoretical electrical engineering' Omsk Russia Department 'Automation and control systems' Omsk Russia 'Theoretical electrical engineering' Omsk Russia

The article discusses the use of discrete wavelet transform in diagnosing power oil-filled transformers using automated acoustic monitoring tools. The article is based on the results of experiments using a simulator with an expanded set of defects. Comparison of acoustic signals on a power transformer with signals from a defect simulator made it possible to formulate a criterion for the presence of an insulation defect. An experimental test of the proposed criterion was carried out on signals of various powers and using electrodes of various shapes. The paper presents expressions that implement the criterion for recognizing the type and degree of development of an insulation defect in power transformers. Examples of processing acoustic signals using wavelet transformation in the MatLab program are given. For each type of signals considered, numerical values of the defect recognition criterion are given. At the end of the article, conclusions and prospects for further development of the proposed approach are formulated. © 2024 IEEE.

关键词： MATLAB

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Electrical tree degradation of MgO/epoxy resin composites at different voltage frequencies

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High Voltage 2024年第3期9卷 581-590页

作者： Yunxiao Zhang Wenxin Lin Yuanxiang Zhou Weiwei Xing Jiayu Cheng Chenyuan Teng College of Electrical Engineering and Automation Fuzhou UniversityFujian Key Laboratory of New Energy Generation and Power ConversionFuzhouChina State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina

Electrical tree degradation is one of the main causes of insulation failure in high-frequency *** tree degradation is studied on pure epoxy resin(EP)and MgO/EP composites at frequencies ranging from 50 Hz to 130 *** results show that the tree initiation voltage of EP decreases,while the growth rate and the expansion coefficient increase with ***,the bubble phenomenon at high frequencies in EP composites is *** with trap distribution character-istics within the material,the intrinsic mechanism of epoxy composites to inhibit the growth of the electrical tree at different frequencies is *** can be concluded that more deep traps and blocking effect are introduced by doping nano-MgO into EP bulks,which can improve the electrical tree resistance performance of EP composites in a wide frequency range.

关键词： composites tree Electrical

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HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 189-198页

作者： GUAN Qi SHENG Zihao XUE Shibei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

关键词： Knowledge engineering Computational modeling Pose estimation Grasping Benchmark testing Feature extraction Real-time systems

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360-Degree Sensor Integration to Optimize Reinforcement Learning for Autonomous Vehicles 14

360-Degree Sensor Integration to Optimize Reinforcement Lear...

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14th Asian control Conference, ASCC 2024

作者： Mardhatillah, Irina Nazaruddin, Yul Yunazwin Widyotriatmo, Augie Institut Teknologi Bandung Instrumentation Control and Automation Research Group Department of Engineering Physics Bandung Indonesia

ISBN: (纸本)9789887581598

The prevalent use of front-view and top-view cam-eras in reinforcement learning (RL)-based autonomous vehicle agents introduces limitations in observing the surroundings. To address these challenges, we propose the integration of 360-degree sensors on the agent, ensuring comprehensive observation cover-age. Our approach involves strategically placing four cameras on the vehicle's sides, supplemented by Light Detection and Ranging (LiDAR) technology to eliminate blind spots. Deep reinforcement learning (deep RL) is employed to develop a driving policy controlling the agent's velocity and steering based on the sensor information. Two sensor configurations are explored, namely the common top-view camera and our 360-degree sensor integration. Simulation results demonstrate excellent performance for the agent equipped with our proposed sensor integration, particu-larly in complex traffic scenarios. The evaluation matrix reveals improved route completion rates, reduced collisions, and lower average collision impulse, underscoring the effectiveness of our proposed sensor configuration. © 2024 Asian control Association.

关键词： Deep reinforcement learning

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of Systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Process Monitoring Based on Temporal Feature Agglomeration and Enhancement

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 825-827页

作者： Xiao Liang Weiwu Yan Yusun Fu Huihe Shao Department of Automation the School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Automation the School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

Dear Editor,This letter proposes a process-monitoring method based on temporal feature agglomeration and enhancement,in which a novel feature extractor called contrastive feature extractor(CFE)extracts the temporal and relational features among process *** the feature representations are enhanced by maximizing the separation among different classes while minimizing the scatter within each class.

关键词： among while letter

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