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IEEE International Conference on Automatic control and Intelligent systems (I2CACIS)

作者： Jie Ying Tan R.M.T Raja Ismail Institute of Postgraduate Studies Universiti Malaysia Pahang Pekan Pahang Malaysia Robotics Intelligent Systems & Control Engineering (RISC) Research Group Faculty of Electrical & Electronics Universiti Malaysia Pahang Pekan Pahang Malaysia

This paper implements a thermal control and a super-twisting sliding mode (STSM) incorporated with an interleaved boost converter (IBC) for an open-cathode proton exchange membrane fuel cell (OC-PEMFC). The implementation of the thermal control is to regulate the stack temperature and adjust the air stoichiometric during the fuel cell current variation. The STSM controller is designed to ensure the fuel cell system’s robustness by achieving the reference values set. Therefore, simulation results discussed that the thermal control provides the suitable stack temperature to the system and maintains the fan voltage during temperature variations. A PI controller is designed and used as a comparison to the proposed STSM controller. Hence, the proposed STSM demonstrates its effectiveness in tracking down the reference current values for the fuel cell system.

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Model-Free Predictive control for Harmonic Suppression of PMSMs Based on Adaptive Resonant controller

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IEEE Transactions on Energy Conversion 2025年

作者： Li, Teng Sun, Xiaodong Su, Zonghao Zha, Xuhao Dianov, Anton Prakht, Vladimir Demidova, Galina Ma, Jinlin Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Jiangsu University School of Automotive and Traffic Engineering Zhenjiang212013 China

To simultaneously suppress the periodic and aperiodic disturbances experienced by permanent magnet synchronous motors in model predictive control, a model-free predictive current control with adaptive resonance control based on an ultra-local model is proposed. Firstly, the ultra-local models of the speed loop and the current loop are established respectively, and the unmodeled and uncertain unknown part F in the ultra-local model is estimated by using a high-order fast terminal sliding mode observer, and its stability is analyzed. Then, an adaptive resonant controller (ARC) is designed to suppress the periodic disturbance caused by the dead zone effect. The ARC and the model-free control based on the ultra-local model are combined to compensate for their respective shortcomings, achieving the suppression of periodic and aperiodic disturbances in the system. Finally, by comparing with the traditional duty cycle model predictive control and the model-free model predictive control without ARC, the proposed scheme is proved to be robust to periodic and aperiodic disturbances. © 2025 IEEE. All rights reserved.

关键词： Permanent magnets

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Nonlinear Model Predictive control Approximation: Applications to Truck-Trailer control System

Nonlinear Model Predictive Control Approximation: Applicatio...

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International Conference on control, Automation and systems ( ICCAS)

作者： Suyong Park Duc Giap Nguyen Yongsik Jin Jinrak Park Dohee Kim Jeong Soo Eo Kyoungseok Han Department of Automotive Engineering Hanyang University Seoul Republic of Korea Department of Mechanical Engineering Kyungpook National University Daegu Republic of Korea Robotics IT Convergence Research Section Electronics and Telecommunications Research Institute (ETRI) Daegu Republic of Korea Department of Electrified Systems Control Research Lab Hyundai Motor Company Hwaseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.

关键词： Training Tracking Simulation Training data Artificial neural networks Predictive models control systems Real-time systems Safety Predictive control

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AGV Semantic Attack Detection Based on Hidden Markov Model

AGV Semantic Attack Detection Based on Hidden Markov Model

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Zhang, Sichao Liang, Wei Zhang, Yinlong Pang, Zhibo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Technology Abb Corporate Research Sweden Department of Automation Vasteras72178 Sweden

ISBN: (纸本)9781728119489

Automatic Guided Vehicle (AGV) semantic attack is now becoming an important research topic in digital workshops. Unfortunately, the conventional attack detections could trigger the wrong attack detection alarms, because of the unexpected wireless radio frequency interference. This paper investigates a new method for AGV semantic attack detection based on Hidden Markov Model (HMM). Our method is able to discriminate the typical wireless retransmission in presence of environment interference or in the context of AGV semantic attack by comparing the hidden state transition probability with the predefined threshold. Our model has been evaluated on our developed platform. The extensive experimental results and analysis prove the effectiveness of our method. © 2022 IEEE.

关键词： Semantics

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Improving generative model-based unfolding with Schrödinger bridges

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Physical Review D 2024年第7期109卷 076011-076011页

作者： Sascha Diefenbacher Guan-Horng Liu Vinicius Mikuni Benjamin Nachman Weili Nie Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology Atlanta Georgia 30332 USA National Energy Research Scientific Computing Center Berkeley Lab Berkeley California 94720 USA Berkeley Institute for Data Science University of California Berkeley California 94720 USA Machine Learning Research Group NVIDIA Research

Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area; one based on discriminative models and one based on generative models. The main advantage of discriminative models is that they learn a small correction to a starting simulation while generative models scale better to regions of phase space with little data. We propose to use Schrödinger bridges and diffusion models to create sbunfold, an unfolding approach that combines the strengths of both discriminative and generative models. The key feature of sbunfold is that its generative model maps one set of events into another without having to go through a known probability density as is the case for normalizing flows and standard diffusion models. We show that sbunfold achieves excellent performance compared to state of the art methods on a synthetic Z+jets dataset.

关键词： Machine learning Particle detectors Probability theory

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Sallen-Key High-Pass Filter with Unrelated Tune of Generic Parameter 23

Sallen-Key High-Pass Filter with Unrelated Tune of Generic P...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Denisenko, Darya Yu. Prokopenko, Nikolay N. Ivanov, Yuri I. Butyrlagin, Nikolay N. Don State Technical University Department of Scientific Research Rostov-on-Don Russia Southern Federal University Department of Automatic Control Systems Rostov-on-Don Russia Department of Information Systems and Radio Engineering Don State Technical University Zelenograd Rostov-on-Don Russia Institute for Design Problems in Microelectronics of Russian Academy of Sciences Zelenograd Rostov-on-Don Russia

ISBN: (纸本)9781665498043

New second order high-pass filter of the Sallen-Key family circuit is considered, where there is unrelated tune of generic parameters by a digitally controlled resistor - the pole frequency, Q-factor pole and the scale transfer coefficient. The basic mathematical expressions of transfer function for proposed high-pass filter are obtained. A comparison of the parameters of the known and new schemes is given. A Sallen-Key high-pass filter circuit based on a high-frequency emitter follower has been developed. The results of high-pass filter modeling in the Micro-Cap 12 environment using the OP37 operational amplifier and an ideal buffer amplifier are presented. Computer simulation has shown that the pole frequency is adjusted by resistor R5. Adjusting the pole attenuation is possible by changing the resistor R3, while the pole attenuation frequency unchanged, an increase in R3 leads to an increase of the pole attenuation. © 2022 IEEE.

关键词： Operational amplifiers

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A Digital Twin Application Framework in the Field of Industry

A Digital Twin Application Framework in the Field of Industr...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xianna Yang Lei Yang Sijia Li China Industrial Control Systems Cyber Emergency Response Team Beijing China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Jiaxing Research Institute Zhejiang University Jiaxing China

Digital twin is a vital technology for the company to make the digital transformation. With the development of big data, industrial Internet of Things (IoT), industrial internet, cloud computing, Augmented Reality/ Virtual Reality (AR/VR) and Artificial Intelligence (AI) etc., digital twin becomes more and more important in the process of product design, production, equipment operation as well as maintenance, help companies to save huge number of cost and time. By using the mechanism model, real-time sensor data and historical data as well as integrating the expert knowledge, digital twin can achieve high-precision, high credibility and high reliability mapping, simulation, prediction, optimization, evaluation to support companies make faster and more correct decision. In this paper, the present theoretical research and industrial application of digital twin at first is discussed, then a framework and flowchart of digital twin application in the field of industry is proposed. At the end, a case from a bearing company is studied to verify the proposed procedure. With the help of digital twin project implementation, the process details are monitored, the digital model and physical model is interacted. Then according to the process characters as well as data collected in real-time, process is optimized.

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Camera-LiDAR Jaywalking Detection in Traffic Surveillance System

Camera-LiDAR Jaywalking Detection in Traffic Surveillance Sy...

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IEEE/SICE International Symposium on System Integration

作者： Taek-Lim Kim Byung Jin Jang Joo Yeon Yeon Tae-Hyeong Kim Tae-Hyoung Park Dept. of Robot Control Eng Chungbuk National University Cheongju Republic of Korea Dept. of Intelligent Systems and Robotics Chungbuk National University Cheongju Republic of Korea Dept. of Research & Development Korea Intelligent Automotive Parts Promotion Institute (KIAPI) Daegu Republic of Korea

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Roadside sensors like cameras and LiDAR enhance pedestrian safety by providing comprehensive traffic data. While traditional traffic surveillance systems primarily focus on vehicle-related violations, such as signal violations and speeding, pedestrian jaywalking remains a significant cause of accidents. This paper presents a jaywalking detection method that fuses camera-based image segmentation with LiDAR ground segmentation to handle various conditions, including day and night. Our system addresses challenges such as poor lighting and vehicle occlusion by leveraging LiDAR’s robustness in unlearned environments. Road segmentation is enhanced by combining camera outputs with LiDAR ground data, refining road boundary detection for more accurate road area analysis. By integrating road segmentation and object tracking, the system reduces false negatives and improves jaywalking detection. Experimental results from real-road data validate its effectiveness, showing significant potential to enhance traffic surveillance.

关键词： Image segmentation Laser radar Pedestrians Roads Surveillance Transportation System integration Traffic control Cameras Sensors

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Large receptive field strategy and important feature extraction strategy in 3D object detection

arXiv

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arXiv 2024年

作者： Cui, Leichao Li, Xiuxian Meng, Min Jia, Guangyu Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201800 China The Centre for Robotics Research Department of Engineering King’s College London Strand LondonWC2R 2LS United Kingdom

The enhancement of 3D object detection is pivotal for precise environmental perception and improved task execution capabilities in autonomous driving. LiDAR point clouds, offering accurate depth information, serve as a crucial information for this purpose. Our study focuses on key challenges in 3D target detection. To tackle the challenge of expanding the receptive field of a 3D convolutional kernel, we introduce the Dynamic Feature Fusion Module (DFFM). This module achieves adaptive expansion of the 3D convolutional kernel’s receptive field, balancing the expansion with acceptable computational loads. This innovation reduces operations, expands the receptive field, and allows the model to dynamically adjust to different object requirements. Simultaneously, we identify redundant information in 3D features. Employing the Feature Selection Module (FSM) quantitatively evaluates and eliminates non-important features, achieving the separation of output box fitting and feature extraction. This innovation enables the detector to focus on critical features, resulting in model compression, reduced computational burden, and minimized candidate frame interference. Extensive experiments confirm that both DFFM and FSM not only enhance current benchmarks, particularly in small target detection, but also accelerate network performance. Importantly, these modules exhibit effective complementarity. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot

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Frontiers of Information Technology & Electronic Engineering 2022年第8期23卷 1217-1228页

作者： Manjun TIAN Xiali LI Shihan KONG Licheng WU Junzhi YU First Research Institute of the Ministry of Public Security of PRC Beijing 100048China School of Information Engineering Minzu University of ChinaBeijing 100081China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our *** propose a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object ***,the YOLOv4 algorithm is chosen as a basic neural network framework to perform object *** the purpose of further improvement on the detection accuracy,YOLOv4 is transformed into a four-scale detection *** improve the detection speed,model pruning is applied to the new *** virtue of the improved detection methods,the robot can collect garbage *** detection speed is up to 66.67 frames/s with a mean average precision(mAP)of 95.099%,and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

关键词： Object detection Aquatic environment Garbage cleaning robot Modified YOLOv4

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