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Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear systems

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

关键词： Adaptive critic control neural network (NN) nonaffine nonlinear systems online fault-tolerant tracking design uniform ultimate boundedness

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Hierarchical Learning-Based Integrated Robust Optimal Control for Nonlinear systems

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3119-3129页

作者： Zhang, Jiacheng Wang, Jingjing Han, Honggui Hou, Ying Huang, Yanting Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

The optimal control of nonlinear systems is crucial to improve system performance. However, the uncertainties of cost functions and systems dynamics make it difficult to solve the optimal control laws. To cope with this problem, a hierarchical learning-based integrated robust optimal control (HL-IROC) method is proposed in this article. The merits of the proposed HL-IROC method are three aspects: First, a hierarchical learning-based integrated optimal control (HL-IOC) scheme, contains a system dynamic learning layer and a cost function learning layer, is designed to transform the original optimal control problem into an integrated optimization problem with control laws as decision variables. Then, the relationships between cost functions and control laws are captured to overcome the difficulties brought by uncertainties in the optimal control process. Second, a global-local cooperative robust evolutionary optimization (GL-CREO) algorithm is proposed to obtain the optimal control laws. Then, a global-local robust searching strategy is employed to deal with two types of uncertainties for improving the robustness of control laws. Third, the convergence analysis of HL-IOC and GL-CREO is discussed in theory. In the experiments, the effectiveness of HL-IROC is illustrated with a nonlinear system and a wastewater treatment process. © 2013 IEEE.

关键词： Optimal control systems

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Conditional Diffusion Model for Skeleton-based Gesture Recognition with Severe Occlusions

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IEEE Signal Processing Letters 2025年 32卷 1970-1974页

作者： Liu, Jinting Gan, Minggang Du, Yao Guan, keyi Guo, Jia Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing China

In the field of skeleton-based gesture recognition, occlusion remains a significant challenge, significantly degrading performance when key joints are occluded or disturbed. To tackle this issue, we propose DiffTrans, a practical conditional diffusion model for occlusion recognition, which enables skeleton-based gesture recognition under high occlusion by generating more likely samples. This study addresses the hand skeleton occlusion problem by framing it as a conditional denoising problem, where unoccluded data serve as observations and occluded data as repair targets. We employ a conditional diffusion model to impute the missing skeleton data and the DSTANet model, which is based on the transformer, to learn the skeleton feature representations. Research results show that the DiffTrans outperforms existing methods under various occlusion modes, maintaining high performance even in scenarios with a high missing rate. © 1994-2012 IEEE.

关键词： Musculoskeletal system

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Experimental study on visual recognition characteristics of traffic signs on mountainous roads by novice drivers

Advances in Transportation Studies

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Advances in transportation Studies 2025年 65卷 69-86页

作者： Meng, Y.W. Quan, Z.Y. Wang, Z.X. Li, S.B. Qing, G.Y. Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System Chongqing Jiaotong University Chongqing400074 China Co. Ltd. Chongqing400067 China

With the expansion of road networks and transportation infrastructure in the mountainous regions, the potential for traffic risks has increased. To address this issue, an array of traffic signs has been deployed. However, the effectiveness of these signs in conveying information to drivers remains uncertain. To better cater to the diverse array of traffic sign settings on mountain roads, a study was conducted, integrating information theory with psychological experimentation. Novice drivers participated in simulations to measure their reaction time (RT) and accuracy (ACC) in response to varying traffic sign densities (TSID). The study found that when TSID ≤ 13.64 bit/m2, drivers exhibited an RT ≤ 1400 ms, indicating manageable workload levels conducive to safe driving tasks on mountainous roads. For scenarios where individual traffic signs displayed ≤ 6 information items and TSID ≤ 15.5 bit/m2, ACC was ≥ 80%, ensuring reliable information processing. In cases involving multiple information items on a single traffic sign, it was recommended that the total number not exceed 7 to avoid excessive cognitive load during driving. These findings offer theoretical insights to enhance the design and deployment of traffic signs on mountainous roads, aiming to improve overall road safety. © 2025, Aracne Editrice. All rights reserved.

关键词： Traffic signs

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A comparison on the effects of coal fines and sand fouling on the shear behaviors of railway ballast using large scale direct shear tests

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Journal of Traffic and transportation Engineering(English Edition) 2025年第1期12卷 52-67页

作者： Xu Zhang Shunying Ji Yang Feng Yongjun Li Chunfa Zhao School of Civil and Transportation Engineering Guangdong University of Technology State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of Technology

Railway ballast is easily contaminated by different fouling material and the fouled ballast always causes the deterioration of railway track. Many previous studies focused on the effect of a certain kind of fouling material on ballast performance, but few studies compared the influence of different fouling material. This paper presents a series of direct shear tests on the clean ballast and the fouled ballast mixed with coal fines or sand to understand the effects of different fouling material. A unified empirical model is proposed to well capture the shear stress-displacement responses of the clean and fouled *** equations are also obtained to characterize the variation of the nonlinear shear strength envelope of the fouled ballast with the fouling content. Based on these equations,a simple method is proposed to predict the shear strength of the fouled ballast using the shear test results of clean ballast. The measured results demonstrate significant differences between the effects of coal fines and sand on the shear behaviors of ballast, to which particular attention should be paid in order to take targeted maintenance measures for the fouled ballast. The mechanisms for the different effects of coal fines and sand are also analyzed to understand the roles that various fouling material plays in the fouled ballast.

关键词： Railway ballast Fouled ballast Shear strength Direct shear test Contamination

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Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation

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Computers, Materials & Continua 2025年第2期82卷 2537-2554页

作者： Wenkai Zhang Hao Zhang Xianming Liu Xiaoyu Guo Xinzhe Wang Shuiwang Li College of Computer Science and Engineering Guilin University of TechnologyGuilin541006China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of TechnologyGuilin541004China

Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.

关键词： Deep learning low-light image enhancement real-time processing knowledge distillation

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(Information Sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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IMPROVED YOLOV8-ALGORITHM FOR SORTING FRESH WHITE TEA: COMBINING FEATURE ENHANCEMENT AND ATTENTION MECHANISM

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INMATEH - Agricultural Engineering 2025年第1期75卷 950-961页

作者： Yu, Xuedong Niu, Yadong Zhou, Zhongyou Lu, Bo Jin, Kaiqiang Wang, Rongyang College of Intelligent Manufacturing and Elevator Technology Huzhou Vocational and Technical College Huzhou China The Huzhou Key Laboratory of Robot System Integration and Intelligent Equipment Huzhou China

In this paper, an improved intelligent sorting algorithm for YOLOv8 white tea fresh leaves is proposed to solve the problems of unclear tea grades and uneven product levels caused by mechanical picking. The algorithm introduces the Dynamic Snake Convolution module (DSConv) for feature enhancement and adds an attention mechanism module, the Multi-Head Self-Attention mechanism (MHSA). Experiments show that the YOLOv8-DsConv-MHSA algorithm has an average accuracy of 96.4% and an average detection rate of 126.6 FPS per second, which is the best algorithm for white tea fresh leaf sorting in the comprehensive comparison. After deploying the proposed YOLOv8-DsConv-MHSA algorithm onto the developed tea sorting machine and conducting experimental comparisons with existing tea sorting machines, it is evident that the screening rate has been enhanced by 10.7%, and the operational efficiency has increased by 20%. © (2025), (INMA Bucharest). All rights reserved.

关键词： Dynamic Snake Convolution module Multi-Head Self-Attention mechanism tea sorting YOLOv8

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DCM-based dynamic stable walking under terrain-induced time-varying disturbances for humanoid robots

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Science China(Technological Sciences) 2025年第4期68卷 105-117页

作者： Yan LUO Chao LIU Jianhua WU Zhenhua XIONG Han DING State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China

When humanoid robots attempt to walk on terrain such as shaking platforms,time-varying disturbances are introduced to the support *** abrupt changes of inclination angle can cause the robot to lose balance upon landing,presenting significant challenges for balance control *** address this issue,we propose a novel divergent component of motion(DCM)-based time-varying disturbance walking(DCM-TVDW)*** method allows the robot to walk on rugged surfaces and helps to maintain dynamic balance when subjected to large time-varying *** the DCM-TVDW control method,we first adjust the robot's center of mass and stride height to adapt to transitions between different terrain types via a variable height stabilization method,and hold these quantities constant as base *** then combine DCM with the N-step capturability *** combination allows for dynamic balance through multi-step adjustments from the initially unstable region,thereby extending the robots stability *** and experimental results demonstrate that the DCM-TVDW method enables the SJ-Bruce robot to traverse a dynamically shaking platform with an inclination angle of approximately 22°.

关键词： humanoid robot dynamic balance control capturability time-varying disturbance

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Energy Consumption Analysis of Flexible Train Composition in Urban Rail Transit 11th

Energy Consumption Analysis of Flexible Train Composition in...

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11th International Conference on Traffic and transportation Studies, ICTTS 2024

作者： Ran, Xinchen Wei, Qinwen Zou, Qingru Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System Chongqing Jiaotong University Chongqing400074 China

ISBN: (纸本)9789819796434

This paper focuses on the energy consumption of urban rail transit trains with flexible train compositions. An energy-efficient speed profile model is proposed to calculate the motion state of various train types. Considering the traction energy consumption and regenerative braking energy, an energy consumption model is established to assess the total energy consumption under a given flexible train service plan and timetable. A set of numerical experiments based on real metro line data is conducted to analyze the energy consumption of different train operation scenarios. The results show that the energy consumption of the flexible train composition mode is reduced by 11.70% compared to the fixed train composition mode, highlighting its potential for enhancing energy efficiency and sustainability in urban rail transit systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Urban transportation

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