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arXiv 2022年

作者： Zhu, Qunxi Shen, Yifei Li, Dongsheng Lin, Wei Research Institute of Intelligent Complex Systems Fudan University China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong Microsoft Research Asia China School of Mathematical Sciences State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Shanghai Center for Mathematical Sciences Center for Computational Systems Biology LCNBI and Research Institute of Intelligent Complex Systems Fudan University China

Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neural network, called the Neural Piecewise-Constant Delay Differential Equations (PCDDEs). Here, unlike the recently proposed framework of the Neural Delay Differential Equations (DDEs), we transform the single delay into the piecewiseconstant delay(s). The Neural PCDDEs with such a transformation, on one hand, inherit the strength of universal approximating capability in Neural DDEs. On the other hand, the Neural PCDDEs, leveraging the contributions of the information from the multiple previous time steps, further promote the modeling capability without augmenting the network dimension. With such a promotion, we show that the Neural PCDDEs do outperform the several existing continuousdepth neural frameworks on the one-dimensional piecewiseconstant delay population dynamics and real-world datasets, including MNIST, CIFAR10, and *** Codes 34Kxx, 93Cxx, 92B2 © 2022, CC BY.

关键词： Ordinary differential equations

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Path Planning for Distribution Network Robots with Adaptive Simulated Annealing-Enhanced Artificial Potential Field Method 26

Path Planning for Distribution Network Robots with Adaptive ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhang, Baichao Chen, Xin Chen, Jinge Li, Jinbin Wang, Xiaokai Jian, Xu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan430077 China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan430077 China

ISBN: (纸本)9798331521950

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. Simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications. © 2025 IEEE.

关键词： Motion planning

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Distributed Stochastic Optimization for Non-Smooth and Weakly Convex Problems under Heavy-Tailed Noise

arXiv

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arXiv 2025年

作者： Hu, Jun Sun, Chao Chen, Bo Wang, Jianzheng Wang, Zheming Department of Automation Zhejiang University of Technology Hangzhou310023 China Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems Hangzhou310023 China

In existing distributed stochastic optimization studies, it is usually assumed that the gradient noise has a bounded variance. However, recent research shows that the heavy-tailed noise, which allows an unbounded variance, is closer to practical scenarios in many tasks. Under heavy-tailed noise, traditional optimization methods, such as stochastic gradient descent, may have poor performance and even diverge. Thus, it is of great importance to study distributed stochastic optimization algorithms applicable to the heavy-tailed noise scenario. However, most of the existing distributed algorithms under heavy-tailed noise are developed for convex and smooth problems, which limits their applications. This paper proposes a clipping-based distributed stochastic algorithm under heavy-tailed noise that is suitable for non-smooth and weakly convex problems. The convergence of the proposed algorithm is proven, and the conditions on the parameters are given. A numerical experiment is conducted to demonstrate the effectiveness of the proposed algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Hierarchical Safe Control of Multiagent systems With Malicious Attacks Using Adaptive Fault-Tolerant Consensus Control

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Pu Zhang, Kun Huang, Yulong Ni, Junkang Lv, Yuezu Gong, Xin Duan, Haibin Xi'an University of Technology School of Automation and Information Engineering Xi'an710048 China Beihang University School of Astronautics Beijing100191 China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China Northwestern Polytechnical University School of Automation Xi'an710072 China Beijing Institute of Technology MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Beihang University School of Automation Science and Electrical Engineering Beijing100083 China

This paper proposed a hierarchical control framework that integrates an upper observer layer and a lower tracking layer. Based on higher-order sliding mode observer in the upper layer, a distributed high-order sliding mode observer (SMO) is used to approximate the leader's state, which can also identify some unknown and uncertain terms caused by malicious attacks. For the lower tracking layer, an adaptive fixed-time prescribed performance fault-tolerant control (FTPPFTC) method is leveraged to address the problem of malicious attacks and help the following agent achieve the stable tracking performance. By using the developed method, it is proved that the tracking errors of the multi-agent systems can converge into a small neighborhood within the fixed time. Finally, the numerical examples and comparative results illustrate the effectiveness of the proposed control method. © 1965-2011 IEEE.

关键词： Sliding mode control

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Load Prediction Model of Gas Boiler Generator Set Based on CNN-LSTM 11th

Load Prediction Model of Gas Boiler Generator Set Based on...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Xu, Yan Wu, Min Hu, Jie Peng, Fu-Sheng Zhang, Wen Song, Wen-Shuo Li, Hui-Hang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789819647521

With the proposal of carbon peak and carbon neutrality targets and the construction of a new power system, improving energy efficiency and reducing carbon emissions have become the key to the sustainable development of industrial boiler generator sets. However, the current load scheduling of gas boiler power plants is mainly based on manual experience, which limits achieving more efficient and stable operation. Accurate short-term load forecasting can help dispatchers to make reasonable production schedules. Therefore, this paper proposes a hybrid load forecasting model for generator sets based on convolutional neural networks combined with long short-term memory networks. First, the Isolation Forest algorithm eliminates anomalies in the historical data set. Then, variables related to unit load are selected by combining mechanistic analysis with the Spearman algorithm. Considering the multiple operating conditions and high complexity of unit loads, the CNN-LSTM network is used to fully extract the spatial and temporal characteristics to build the load prediction model. Finally, experiments are conducted using actual production data, showing that the proposed method is effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric load dispatching

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Inverse Covariance Intersection Fusion for Lie-Group-Based Pose Estimation

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IEEE Signal Processing Letters 2025年

作者： Zou, Tao Wang, Rusheng Chen, Bo Hu, Zhongyao Zhejiang University of Technology Department of Automation Hangzhou310023 China Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems Hangzhou310023 China

This letter is concerned with the pose estimation problem on Lie groups. In general, robots naturally move on the special Euclidean Lie groups, which provides the motivation to model the measurement uncertainty on Lie algebras and project it onto Lie groups. Then, a Lie-group-based pose estimation method under inverse covariance intersection fusion is proposed, in which the obtained estimates are more accurate than in vector spaces. Since the unknown common information shared among the measurements is taken into account, the proposed Lie-group-based pose estimation method has the advantages of higher precision and better consistency. Finally, the effectiveness of the proposed method is verified through simulations. © 1994-2012 IEEE.

关键词： Lie groups

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Multi-agent Traffic Prediction via Denoised Endpoint Distribution

Multi-agent Traffic Prediction via Denoised Endpoint Distrib...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Yao Liu Ruoyu Wang Yuanjiang Cao Quan Z. Sheng Lina Yao Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China School of Computing Macquarie University Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Data 61 CSIRO & School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatiotemporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents’ spatio-temporal features alongside their intrinsic intentions and un-certainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model’s efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.

关键词： Uncertainty Navigation Soft sensors Robot kinematics Predictive models Transformers Road traffic Trajectory complexity theory Spatiotemporal phenomena

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese Automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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Multicarrier Waveform Design for mmWave/THz Integrated Sensing and Communication

Multicarrier Waveform Design for mmWave/THz Integrated Sensi...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Fan Zhang Tianqi Mao Ruiqi Liu Leyi Zhang Dezhi Zheng Tengfei Wang Zhaocheng Wang Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing China Yangtze Delta Region Academy Beijing Institute of Technology (Jiaxing) Jiaxing China Wireless and Computing Research Institute ZTE Corporation Beijing China Technology Planning Department ZTE Corporation Beijing China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Integrated sensing and communication (ISAC) is recognized as one of key enabling technologies for the Meta-verse. To enhance both communication data rate and sensing accuracy, the exploitation of millimeter wave (mmWave) and terahertz (THz) frequencies becomes mandatory due to huge amount of spectrum resources. To combat the severe path-loss at mmWave/THz band, large-scale antenna arrays are usually employed to form directional beams. However, the ultra-broad bandwidth induces undesirable beam squint (BS) effects, where the beams from different subcarriers point to diverse angles, leading to the communication performance loss. Fortunately, this BS effect can be leveraged to facilitate the multi-angle super-resolution sensing with minimal beam sweeping overhead. Against this background, we propose a novel multicarrier waveform design methodology for the BS-assisted ISAC systems, which optimizes the frequency resource allocation between sensing and communications to reach a good dual-functional performance trade-off. To mitigate mutual interference, both functions are assigned non-overlapping subcarriers. The subcarrier assignment design is formulated as a mixed integer programming problem to maximize the communication throughput while ensuring the required sensing range and resolution, which involves high complexity to get an exact solution. To this end, we propose a two-stage iterative update algorithm to obtain a quasi-optimal solution with low computational complexity. Numerical results demonstrate that our proposed methodology achieves high-rate communication and high-resolution sensing simultaneously with relatively low overhead.

关键词： Wireless communication Design methodology Superresolution Integrated sensing and communication Throughput Iterative algorithms Resource management

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive Control 26

Trajectory Tracking for Directional Drilling in Underground ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wu, Jiajun Lu, Chengda Li, Hao Wei, Hongchao Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an710077 China China Coal Research Institute Beijing100013 China

ISBN: (纸本)9798331521950

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the H∞ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment. © 2025 IEEE.

关键词： Predictive control systems

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