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Asian control Conference

作者： Yu Zhu Haodong Chen Xihong Fei Kang Wang Jing Wang School of Electrical and Information Engineering Anhui University of Technology Ma'anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The initial angle detection and tracking control of brushless direct current motor (BLDC) is widely used in military, aviation, industrial, automotive, civil control systems, and household appliances. The motor is a nonlinear system, which is easily affected by external factors, so it is difficult to maintain the stability of the system with fixed control parameters. Therefore, this paper proposes a control method for initial angle tracking of BLDC based on reinforcement learning (RL). In order to determine the initial angle of the surface-mounted direct current (DC) motor, in the traditional Field-Oriented control (FOC) algorithm, the pulsating high-frequency injection algorithm is introduced and combined with the RL theory. According to the given PI parameters, the appropriate rewards are obtained through the RL controller. Then the proportional integral (PI) parameters are dynamically adjusted to make the estimated angle closer to the true angle value. This method avoids the error of angle estimation caused by fixed PI parameters in different situations, and obtains the best results by dynamically adjusting the parameters. Finally, the simulation experiment is carried out to prove the feasibility of the proposed scheme.

关键词： Home appliances Brushless DC motors Heuristic algorithms Estimation Reinforcement learning Prediction algorithms control systems

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Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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Dynamic Maintenance Document management Based on BOM 7

Dynamic Maintenance Document Management Based on BOM

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7th International Conference on systems and Informatics, ICSAI 2021

作者： Zhou, Chunliu Ye, Shan Wang, Hongjun Gao, Zhenhua Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Key Laboratory of Multidisciplinary Management Ma'anshan China School of Management Science and Engineering Anhui University of Technology Ma'anshan China

ISBN: (纸本)9781665426244

To realize the efficient management of high-speed train maintenance documents, a document management method based on the Bill of Material (BOM) was proposed. Since the maintenance documents were dynamically changed as the design updated, and the change process was complicated and time-consuming via handwork, the relationship between the Engineering BOM (EBOM) and the Documental BOM (DBOM) was established, which delivered the design change, updated the maintenance documents in real time, and provided the latest documents for on-site maintenance business. Technologies of fuzzy matching, version management and XML were used together to realized the dynamic management of maintenance documents, and an application was displayed with system interfaces to verify the DBOM method. © 2021 IEEE.

关键词： Maintenance

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Talent Prediction Based on Switching Strategy Algorithm of Residual Grey Model

Talent Prediction Based on Switching Strategy Algorithm of R...

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Chinese control and Decision Conference, CCDC

作者： Yuxin Pan Xihong Fei Bo Shan Hao Shen Jing Wang Kang Wang School of Electrical and Information Engineering Anhui University of Technology Ma’Anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’Anshan China Instrumentation Technology and Economy Institute Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The surge in demand for medical equipment necessitates accurate prediction of talent demand in the medical industry, becoming a crucial support for the strategic planning of talent and human resources in universities. However, the difficulty in effectively collecting talent data in the medical equipment industry results in limited availability of talent demand data. At the same time, existing research focuses only on the linear or nonlinear single relationship of talent demand sequence data, thereby reducing the accuracy and robustness of predictions. In this paper, we propose a Switching Strategy Algorithm based on Residual Grey Model (SSA-RGM) to address the above issue. SSA-RGM is a hybrid model that includes a novel residual grey model, commonly used simple linear regression model, and a switching strategy based on optimal fitness. In this model, we introduce a residual operation based on the grey model and, for the first time, incorporate a penalty factor to mitigate the impact of linear shifts in small-sample data within nonlinear systems. Furthermore, we adopt the principle of optimal fitness to select the best model in the hybrid model. Experimental results on six real-world datasets show that SSA-RGM achieves the best talent prediction performance in various settings.

关键词： Industries Biomedical equipment Accuracy Switches Predictive models Strategic planning Prediction algorithms

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Finite-time Robust Fault-tolerant Trajectory Tracking control for QUAVs with Actuator Fault

Finite-time Robust Fault-tolerant Trajectory Tracking Contro...

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Robotics, control and Automation Engineering (RCAE), International Conference on

作者： Rongsheng Xia Chunlei Bu Han Jiang Chery Automobile Stock Limited Company Wuhu China School of Electrical and Information Engineering Anhui University of Technology Ma’Anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’Anshan China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

This paper proposed a finite-time robust fault-tolerant trajectory tracking control strategy for quadrotor unmanned aerial vehicles (QUAVs) suffering from actuator saturation, unknown external disturbance and actuator fault. Firstly, robust compensation terms are used to handle unknown external disturbance in position subsystem. On this basis, parameter adaptive method is introduced to remove the adverse effects of actuator saturation in attitude subsystem. Then, a fault detection mechanism is designed to estimate the actuator fault in real time. Subsequently, a finite-time robust fault-tolerant tracking strategy based on backstepping framework are proposed and and the finite-time convergence of all error signals are ensurend. Finally, a numerical simulation result is displayed to validate the effectiveness of the proposed trajectory tracking control scheme for the QUAVs dynamic system.

关键词： Actuators Fault tolerance Trajectory tracking Fault tolerant systems Stability analysis Real-time systems Robots Numerical stability Quadrotors Convergence

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Effect of New Dual-Credit Policy on Electric Vehicle Diffusion in China

SSRN

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SSRN 2025年

作者： Zhao, Dan Wang, Jian Li, Wen-Wu Huang, Shuai Tang, Jin-Huan School of Management Science and Engineering Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China School of Economics and Management Shenyang Aerospace University Shenyang110136 China School of Business Qingdao University Qingdao266071 China

The impact of new dual-credit policy on electric vehicle (EV) diffusion is examined through complex network evolutionary game theory, we find that merely tightening new energy vehicle (NEV) credit rule and corporate average fuel consumption (CAFC) credit rule may not necessarily benefit the growth of auto industry. This paper further discusses the influence of different update strategies on EV diffusion. The results indicate that with the increase of NEV credit accounting coefficient, the upper limit of threshold interval of NEV credit trading price remains unchanged, while lower limit decreases. With the decrease of ratio of standard CAFC to target CAFC or the increase of requirement of NEV credits for fuel vehicle manufacturer, the upper and lower limits of threshold interval of NEV credit price are reduced. With the increasing network scale, the driving effect of Fermi function is gradually superior to EWA algorithm. © 2025, The Authors. All rights reserved.

关键词： Automotive industry

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Dynamic Maintenance Document management Based on BOM

Dynamic Maintenance Document Management Based on BOM

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International Conference on systems and Informatics (ICSAI)

作者： Chunliu Zhou Shan Ye Hongjun Wang Zhenhua Gao Key Laboratory of Multidisciplinary Management Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan China School of Management Science and Engineering Anhui University of Technology Ma'anshan China

ISBN: (纸本)9781665426251

关键词： Solid modeling Bills of materials XML Data visualization Maintenance engineering Real-time systems Mobile handsets

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Multi-Objective Squirrel Search Algorithm for EEG Feature Selection

SSRN

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SSRN 2022年

作者： Wang, Chao Li, Songjie Shi, Miao Zhao, Jie Acharya, U. Rajendra Xie, Neng-Gang Cheong, Kang Hao College of Civil Engineering and Architecture Anhui Polytechnic University Anhui Wuhu241000 China Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems Anhui Ma’anshan 243002 China Department of Management science and Engineering Anhui University of Technology Anhui Maanshan243002 China 8 Somapah Road Singapore487372 Singapore Department of Mechanical Engineering Anhui University of Technology Maanshan243002 China Department of Electronics and Computer Engineering Ngee Ann Polytechnic S599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Feature selection plays a critical role in the application of Brain Computer Interface (BCI) systems. Many methods have been used to solve the feature selection problem, but they model it as a single-objective problem, considering only classification accuracy or number of features. To close this critical gap, we improve the squirrel search algorithm by combining it with the grid method, and propose a Multi-Objective Squirrel Search Algorithm (MOSSA) to solve the feature selection problem in BCI. We conduct experiments on three publicly available motion imagery datasets, and the experimental results reveal the best classification results of the method on dataset 1. The average classification accuracy of dataset 2 is 96.71%, with the number of selected features reduced to 18 on average. The highest classification accuracy of dataset 3 is 83.57% on the training set and 82.86% on the test set. In addition, we compare MOSSA with other algorithms and the results show the superiority of our proposed method in solving the feature selection problem. Finally, we combine MOSSA with an online application of BCI, where subjects visualise controlling the robot to perform the corresponding actions by the left and right hand movements. The average recognition rate of the three subjects is approximately 70%. In summary, the MOSSA is an effective method for solving the feature selection problem and is useful for the development of online © 2022, The Authors. All rights reserved.

关键词： Brain computer interface

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A KDC-enhanced closest point method for thermal analysis on curved geometries using LS-GFDM

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Applied Mathematics Letters 2025年 171卷

作者： Zhuochao Tang Jiming Gu Bin Chen Zhuojia Fu School of Management Science and Engineering Anhui University of Technology Maanshan 243002 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Maanshan 243002 China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and Engineering Science Hohai University Nanjing 211100 China

This paper presents a Krylov-deferred correction (KDC)-enhanced closest point method (CPM) integrated with the least-squares generalized finite difference method (LS-GFDM) for thermal analysis on curved geometries. To overcome the inherent limitations of conventional single-step/multi-step temporal schemes in long-time simulations, we systematically unify three advanced components: (i) CPM’s embedding strategy that transforms surface PDEs into extended problems over Cartesian grids, (ii) LS-GFDM’s spatial discretization employing dual point sets to enhance applicability beyond conventional GFDM, and (iii) KDC’s predictor–corrector framework that iteratively refines temporal solutions to achieve high-order accuracy. Building upon our previous work (Tang et al. 2025) where LS-GFDM was integrated with CPM using explicit Euler temporal schemes, this study constitutes a critical advancement by replacing low-order temporal discretization with the KDC methodology. Numerical experiments demonstrate that our KDC-CPM-LSGFDM framework achieves superior numerical performance.

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Consider payoff or emphasize learning? Exploring the effect of new dual-credit policy on electric vehicle diffusion in complex network

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Energy Economics 2025年 148卷

作者： Dan Zhao Jian Wang Wen-wu Li Jin-huan Tang Shuai Huang School of Management Science and Engineering Anhui University of Technology Ma'anshan 243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan 243002 China School of Systems Engineering National University of Defense Technology Changsha 410003 China School of Economics and Management Shenyang Aerospace University Shenyang 110136 China School of Business Qingdao University Qingdao 266071 China

In recent years, although the overall quality of the new energy vehicle (NEV) industry has shown an upward trend, it is difficult to ignore the turbulence in the production and sales of NEV. The impact caused by dual credit policy during the transition needs further analysis. In order to clarify the internal operating mechanism of the new dual-credit policy parameters and stably promote the booming development of the automobile industry, we construct a complex network evolutionary game model to analyze the impact of the new dual-credit policy on the electric vehicle (EV) diffusion, and further discuss the influence of different update strategies on EV diffusion. The results indicate that with the increase of NEV credit accounting coefficient, the upper limit of threshold interval of NEV credit trading price remains unchanged, while lower limit decreases. With the decrease of ratio of standard CAFC to target CAFC or the increase of requirement of NEV credits for fuel vehicle manufacturer, the upper and lower limits of threshold interval of NEV credit price are reduced. With the increasing network scale, the driving effect of Fermi function is gradually superior to EWA algorithm. These results not only reveal the complex mechanism of policy parameters on EV diffusion, but also provide theoretical basis and practical guidance for optimizing NEV industry policies.

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