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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Long, Jiang Hu, Yunfeng Gong, Xun College of Telecommunications Engineering Jilin University State Key Laboratory of Automotive Simulation and Control Changchun China College of Artificial Intelligence Jilin University State Key Laboratory of Automotive Simulation and Control Changchun China

ISBN: (纸本)9798350359558

Accurate measurement of current feedback is an important prerequisite for high performance permanent magnet synchronous motor (PMSM) drive systems. For the actual control system, due to the thermal drift, aging, nonlinearity and some other factors of the current sensors and the corresponding conditioning circuits, current measurement error will occur and correspondingly will result in torque and speed ripples. To overcome this issue, an online current measurement error compensation (CMEC) method for PMSM drive system is proposed in this paper. Firstly, the influence of different kinds of current measurement errors, the offset error and scaling error, is analyzed from the perspective of open loop control. Secondly, a Discrete Fourier Transform (DFT) based CMEC method is proposed. Finally, the effectiveness of the proposed method is validated on a 1kW commercialized servo motor test rig, results show that the proposed method can reduce 80% of the current ripple. © 2024 IEEE.

关键词： Discrete Fourier transforms

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Estimating NOx Sensor Ammonia Cross-Sensitivity Factor Using a Data-Driven Approach in SCR Systems 7

Estimating NOx Sensor Ammonia Cross-Sensitivity Factor Using...

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7th CAA International Conference on Vehicular control and intelligence, CVCI 2023

作者： Wang, Ning Gong, Xun Hu, Yunfeng Sun, Yao State Key Laboratory of Automotive Simulation and Control College of Artificial Intelligence Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Telecommunications Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Changchun China

ISBN: (纸本)9798350340488

Selective Catalytic Reduction (SCR) is a module used in the aftertreatment system to mitigate the nitrogen oxides (NOx) emissions, and it can be affected by cross-sensitivity, resulting in a decrease in control performance and even controller failure. A commonly adopted approach is estimating the cross-sensitivity factor to correct the downstream NOx concentration, but the current estimation strategies often incur relatively high costs. In this paper, we propose a novel and more cost-effective method based on data-driven for estimating the cross-sensitivity factor. Specifically, we design a network architecture based on Long Short-Term Memory (LSTM) and attention mechanism. LSTM is utilized to capture the non-linear relationship between input features, historical information, and the cross-sensitivity factor. The introduced attention mechanism allows the model to focus more on important information, thereby further improving the estimation accuracy. This method eliminates the need for complex mechanism modeling and parameter calibration, and reduces the computational burden during online operation, resulting in significant time and computational resource *** simulation experiments conducted transient bench data validate the excellent performance of the designed cross-sensitivity factor estimator, as evidenced by comparative analysis with three alternative methods, and demonstrate the rationality of feature selection through the feature ablation analysis. © 2023 IEEE.

关键词： aftertreatment attention cross-sensitivity factor Long Short-Term Memory

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A Mode-Switching Soot Load Observer for automotive Particulate Filters 7

A Mode-Switching Soot Load Observer for Automotive Particula...

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7th CAA International Conference on Vehicular control and intelligence, CVCI 2023

作者： Lin, Xiangxue Hu, Yunfeng Jiang, Linlin Gong, Xun Sun, Yao State Key Laboratory of Automotive Simulation and Control College of Telecommunications Engineering Jilin University Changchun China FAW-Volkswagen Technology Center Changchun China State Key Laboratory of Automotive Simulation and Control College of Artificial Intelligence Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Changchun China

ISBN: (纸本)9798350340488

In automotive particulate filter applications, accurate real-time soot load is critical to filter regeneration, which significantly affects vehicle safety and economics. Based on the modified soot accumulation and filter pressure drop characteristics, equations of state and measurement have been deduced, and a corresponding novel Luenberger observer is developed. Due to the limitations of the differential pressure sensor, the deviation of its measurement value is small at small exhaust flow rates and can not reflect the soot load. To figure out this issue, a mode-switching strategy is proposed to schedule the correction gain according to the exhaust flow rate. A co-simulation platform has been built to validate the observer, which adopts GT-Power as the test vehicle. Compared with the open-loop observer under single and multiple NEDC cycles, the mode-switching observer shows some improvement, and the maximum deviation is reduced from 0.58g/L to 0.1g/L. Thus, by reducing the times of regenerations, fuel consumption can be saved about 8%. Finally, the proposed observer shows better robustness when a disturbance occurs, the maximum deviation still maintain a low level of about 0.25g/L. © 2023 IEEE.

关键词： Luenberger observer Mode-switching Particulate filter Soot accumulation and regeneration soot load Transient cycle validation

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Noise-Tolerant ZNN-Based Data-Driven Iterative Learning control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 344-361页

作者： Yunfeng Hu Chong Zhang Bo Wang Jing Zhao Xun Gong Jinwu Gao Hong Chen State Key Laboratory of Automotive Simulation and Control Jilin UniversityJilin UniversityChangchun 130025China College of Communication Engineering Jilin UniversityJilin UniversityChangchun 130025China Department of Electromechanical Engineering University of MacaoMacao 999078China School of Artificial Intelligence Jilin UniversityChangchun 130012China IEEE College of Electronics and Information Engineering Tongji UniversityShanghai 200092China

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

关键词： Adaptive control control system synthesis data-driven iterative learning control neurocontroller nonlinear discrete time systems

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Semantic Segmentation-Inspired Prediction of Pavement Adhesion Coefficient 7

Semantic Segmentation-Inspired Prediction of Pavement Adhesi...

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7th CAA International Conference on Vehicular control and intelligence, CVCI 2023

作者： Guo, Hongyan Wan, Juncheng Meng, Qingyu Liu, Yanran State Key Laboratory of Automotive Simulation and Control The College of Communication Engineering Jilin University Changchun China

ISBN: (纸本)9798350340488

Accurate prediction of the adhesion coefficient parameters of the road ahead is critical to the driving safety of intelligent driving vehicles, however, the current stage of raw image-based methods are difficult to achieve high-precision and reliable prediction due to the complexity of the elements within the scene. To this end, a road adhesion coefficient recognition algorithm inspired by semantic segmentation is proposed. The method designs a sequential prediction algorithm structure that includes semantic segmentation, road surface extraction, road surface recognition, and adhesion coefficient mapping. Additionally, the method introduces the spatial attention mechanism and channel attention mechanism separately into the semantic segmentation network and road surface classification network, effectively enhancing the accuracy and real-time performance of the adhesion coefficient prediction algorithm. Experimental results illustrate that this real-time adhesion coefficient prediction algorithm achieves good performance in maintaining accuracy, real-time capability, and robustness. It can accurately and rapidly predict road surface adhesion information in driving scenarios. © 2023 IEEE.

关键词： Attention mechanism Real-time performance Road surface adhesion coefficient Semantic segmentation

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High Robustness Current Measurement Error Compensation of SPMSM Drives Based on Discrete Fourier Transform

High Robustness Current Measurement Error Compensation of SP...

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China International Electrical and Energy Conference (CIEEC)

作者： Jiang Long Yunfeng Hu Xun Gong State Key Laboratory of Automotive Simulation and Control College of Telecommunications Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Artificial Intelligence Jilin University Changchun China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

关键词： Torque Thermal factors Current measurement Discrete Fourier transforms Error compensation Thermal sensors Sensor systems

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Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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Exploring reservoir computing:Implementation via double stochastic nanowire networks

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Chinese Physics B 2024年第3期33卷 572-582页

作者：唐健峰夏磊李广隶付军段书凯王丽丹 College of Artificial Intelligence Southwest UniversityChongqing 400715China Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSouthwest UniversityChongqing 400715China State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing 400715China

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex *** studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data *** paper presents a model based on these nanowire networks,with an improved conductance variation *** suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage *** nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction *** experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction *** results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

关键词： double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction

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Estimating NOx Sensor Ammonia Cross-Sensitivity Factor Using a Data-Driven Approach in SCR Systems

Estimating NOx Sensor Ammonia Cross-Sensitivity Factor Using...

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Conference on Vehicle control and intelligence (CVCI)

作者： Ning Wang Xun Gong Yunfeng Hu Yao Sun State Key Laboratory of Automotive Simulation and Control College of Artificial Intelligence Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Telecommunications Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Changchun China

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Digital CEOs in Digital Enterprises:Automating,Augmenting,and Parallel in Metaverse/CPSS/TAOs

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 820-823页

作者： Juanjuan Li Rui Qin Sangtian Guan Xiao Xue Peng Zhu Fei-Yue Wang with State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Economics and Management Nanjing University of Science and TechnologyNanjing 210094China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan... 详细信息

关键词： exceptional Auto advancing

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