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6th IEEE International electrical and Energy Conference, CIEEC 2023

作者： Cheng, Yuan Lv, Pingtailei Du, Bochao Cui, Shumei Department of Electrical Engineering and Automation China Chongqing Research Institute of Hit Harbin China

ISBN: (纸本)9798350346671

In order to improve the dynamic performance of sensorless Interior Permanent Magnet Synchronous Motor (IPMSM) position observation at low speeds, this paper proposes a position error extraction method based on Luenberger observer and combined with deadbeat predictive current control (DPCC) strategy. To improve the robustness of the position observation, a high frequency square-wave voltage is injected into the d-axis to generate a high frequency current signal with position information. Then the actual current is subtracted from the observed signal to obtain the high frequency current error. The motor position information is calculated by position error decoupling and phase-locked loop. To improve the rapidity of the current loop, a DPCC strategy is adopted to obtain the predicted value of the motor current using the observer. Finally, the simulation results show that the proposed control method can reduce the overshoot of position observation error and the dynamic performance of the motor controller is improved. © 2023 IEEE.

关键词： Sensorless control

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Optimal Energy Management for Multi-Stack Fuel Cell Vehicles based on Hybrid Quantum Reinforcement Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8500-8511页

作者： Shi, Wenzhuo Sun, Xianzhuo Zhang, Zelong Chen, Junyu Du, Yuhua Ruan, Jiaqi Ding, Yibo Wang, Lei Huangfu, Yigeng Xu, Zhao Northwestern Polytechnical University School of Automation Xi'an China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering SAR Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Research Institute of Smart Energy SAR Hong Kong Hong Kong

This paper proposes a driving condition recognition-based (DCR-based) Hybrid Quantum Deep Deterministic Policy Gradient (HQDDPG) method for energy management in multi-stack fuel cell vehicle hybrid power systems (MFCV HPSs) and its quantum simulation setup on Digital Signal Processors (DSPs). Driving conditions are initially segmented into micro-trips and clustered into three types. The DCR method, using a Learning Vector Quantization Neural Network (LVQNN), is then developed, thus accurately and efficiently identifying driving condition types. Subsequently, quantum reinforcement learning is proposed to achieve optimal energy manaement of MFCV HPSS, i.e., power allocation among the multiple fuel cells to minimize the economic metrics based on the DCR results. Compared to classical large-scale neural networks, quantum reinforcement learning reduces parameters by combining a Parameterized Quantum Circuit (PQC) with a single-layer classical neural network. The PQC encodes and processes state information through quantum mechanics for enhanced computational expressiveness, while the classical neural network transforms the quantum measurement expectations into actionable outputs for energy management. The trained hybrid quantum circuits are implemented on DSPs through quantum simulations. The method is validated through Controller Hardware-in-the-Loop (CHIL) experiments, demonstrating superior performance in optimizing economic metrics compared to conventional methods. © 2015 IEEE.

关键词： Reinforcement learning

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Data augmentation and denoising of computed tomography scan images in training deep learning models for rapid COVID-19 detection

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International Journal of Business Intelligence and Data Mining 2024年第2期24卷 203-216页

作者： Mubarak, Auwalu Saleh Serte, Sertan Al-Turjman, Fadi Ameen, Zubaida Sa'Id Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin10 Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin10 Turkey Department of Electrical and Electronics Engineering Near East University Mersin10 Turkey

The deadly respiratory disease corona virus-2 (COVID-19) which was declared a pandemic by the World Health Organization (WHO) has resulted in over a million deaths around the world within less than a year. With the rapid spread of the virus, the currently adopted COVID-19 test by the WHO is the reverse transcription polymerase chain reaction (RT-PCR) test, which is expensive, time-consuming and not accessed by underdeveloped countries. Computed tomography (CT) scan images that were used in profiling suspected COVID-19 patients can serve as an alternative to the RT PCR test method. In this study, two different pre-Trained deep learning models ResNet-50 and ResNet-101 were trained to classify positive COVID-19 scan images. The best model which was trained on the augmented CT scan images achieved an accuracy of 98.3%, a sensitivity of 0.984, specificity of 0.983. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： COVID-19

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Fast Visual Servo for Rapidly Seafood Capturing of Underwater Delta Robots

Fast Visual Servo for Rapidly Seafood Capturing of Underwate...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Yang, Maosheng Xiao, Lin Chen, Ce Hu, Yangyi Sun, Yi Pu, Huayan Jia, Wenchuan Shanghai University School of Mechanical and Electrical Engineering and Automation Department of Automation China Shanghai University Research Institute of Usv Engineering China

ISBN: (纸本)9798350325706

In this paper, we propose and design an underwater delta robot for fast seafood grasping. First, the hardware structure of the robot is described in detail. After that, a visual servo control method for fast catching of this underwater delta robot is proposed. The method is able to generate real-time radial trajectories so as to realize the catching based on the swaying of the robot body as well as the movement of the object to be caught. In the actual grasping test, the moving platform and the slave arm can occlude to the target resulting in the loss of target position information. Therefore, we propose a position prediction method to predict the position of the grasped object when occlusion occurs, thus improving the success rate of grasping and ensuring a smooth robot trajectory. Finally, several land and underwater experiments were conducted with good results, which verified the feasibility of the robot structure and algorithm. © 2023 IEEE.

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Real-Time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes

Real-Time Motion Generation and Data Augmentation for Graspi...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Yamamoto, Kenjiro Ito, Hiroshi Ichiwara, Hideyuki Mori, Hiroki Ogata, Tetsuya Connective Automation Innovation Center R&d Group Hitachi Ltd. Robotics Research Department Japan Institute for Ai and Robotics Future Robotics Organization Japan Waseda University Faculty of Science and Engineering Japan

ISBN: (纸本)9798350312072

While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion teaching in a real environment. The manipulation of moving objects, due to their dynamic properties, requires learning a wide range of factors such as the object's position, movement speed, and grasping timing. We propose a data augmentation method for enabling a robot to grasp moving objects with different speeds and grasping timings at low cost. Specifically, the robot is taught to grasp an object moving at low speed using teleoperation, and multiple data with different speeds and grasping timings are generated by down-sampling and padding the robot sensor data in the time-series direction. By learning multiple sensor data in a time series, the robot can generate motions while adjusting the grasping timing for unlearned movement speeds and sudden speed changes. We have shown using a real robot that this data augmentation method facilitates learning the relationship between object position and velocity and enables the robot to perform robust grasping motions for unlearned positions and objects with dynamically changing positions and velocities. © 2024 IEEE.

关键词： Time series

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BIPEDAL ROBOT RUNNING GAIT PATTERN GENERATION THROUGH LINEAR INVERTED PENDULUM MODEL

BIPEDAL ROBOT RUNNING GAIT PATTERN GENERATION THROUGH LINEAR...

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Wang, Xinhui Zhang, JunHui Zhang, Kang Qi, Pengyuan School of Automation Science and Electrical Engineering Beihang University Beijing China HighTorque Robotics Guangdong Guangzhou China Wuhan Second Ship Design and Research Institute Hubei Wuhan China

This paper tackles the challenge of making bipedal robots run, a task that involves managing a complex movement called the flight phase. We propose a straightforward and effective method using the Linear Inverted Pendulum Model to create stable running gaits. Our approach plans a simple ascending trajectory during the support phase, which provides the necessary upward velocity to a flight phase and enabling the robot to run. This method simplifies the control needed while ensuring the robot can run realistically. Our tests show that this LIPM-based approach allows the robot to run smoothly. Our work suggests that LIPM based gait pattern generator can be a simple strategy for designing running motions in humanoid robots. © The Institution of engineering & Technology 2024.

关键词： Inverted pendulum

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Analysis of the Mechanism and Countermeasures of Harmonics Influence on Inverter in WPT Systems

Analysis of the Mechanism and Countermeasures of Harmonics I...

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International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Cui, Chao Zhu, Chunbo Liu, Guofeng Zhang, Qianfan Chen, Qingquan School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150006 China Zhengzhou Research Institute of Harbin Institute of Technology Zhengzhou450003 China Department of Electrical & Electronic Engineering The University of Hong Kong 710049 China

ISBN: (纸本)9789819620791

Wireless power transmission (WPT) systems are usually analyzed and designed with only the resonant frequency space in mind, whereas in practice the use of square-wave inverter sources introduces harmonics into the system. These harmonics can distort the output current waveform of the inverter source when the filtering performance of the WPT system is poor, a problem that is more likely to occur and is more severe at light loads. The harmonics problem leads to efficiency degradation and control difficulties in the system. This paper analyzes the mechanism of harmonic generation in WPT system, calculates the degree of harmonic influence on the inverter in various aspects, and proposes the methods to eliminate the harmonics in WPT system from various angles. This paper facilitates WPT system designers to avoid the harm of harmonics. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Inductive power transmission

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Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 450-465页

作者： Lujuan Dang Badong Chen Yulong Huang Yonggang Zhang Haiquan Zhao Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’an 710049China IEEE Department of Automation Harbin Engineering UniversityHarbin 150001China School of Electrical Engineering Southwest Jiaotong UniversityChengdu 610000China

Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian *** CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian *** address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is *** MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters *** concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first *** MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression *** the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters *** addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence *** enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.

关键词： Cubature Kalman filter(CKF) inertial navigation system(INS)/global positioning system(GPS)integration minimum error entropy with fiducial points(MEEF) non-Gaussian noise

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Graph neural network based method for robot path planning

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Biomimetic Intelligence & robotics 2024年第1期4卷 80-87页

作者： Xingrong Diao Wenzheng Chi Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Robotics and Microsystems Center School of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215021China Jiaxing Research Institute Southern University of Science and TechnologyJiaxing 314000China

Sampling-based path planning is widely used in robotics,particularly in high-dimensional state *** the path planning process,collision detection is the most time-consuming ***,we propose a learning-based path planning method that reduces the number of collision *** develop an efficient neural network model based on graph neural *** model outputs weights for each neighbor based on the obstacle,searched path,and random geometric graph,which are used to guide the planner in avoiding *** evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world *** results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.

关键词： Graph Neural Network(GNN) Collision detection Sampling-based path planning

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