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Dynamic sequential neighbor processing: A liquid neural network-inspired framework for enhanced graph neural networks

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Information Sciences 2025年 719卷

作者： Kuijie Zhang Shanchen Pang Yuanyuan Zhang Yun Bai Luqi Wang Jerry Chun-Wei Lin State Key Laboratory of Chemical Safety Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software the Qingdao Institute of Software China University of Petroleum Qingdao 266580 China School of information and control engineering Qingdao University of Technology Shandong266520 China Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and IT Devices Silesian University of Technology 44-100 Gliwice Poland

Integrating information from multi-order neighborhoods is a fundamental strategy in Graph Neural Networks (GNNs) for capturing higher-order structural patterns and enhancing the expressive power of node representations. However, most existing GNNs treat neighbors from different orders as unordered sets and integrate them using static or parallel strategies, thus overlooking the sequential and evolving nature of neighborhood expansion. To address this limitation, we propose a novel GNN framework, SL, which integrates Serialized Neighbor Features with Liquid Neural Networks (LNNs) to enable order-aware, dynamic adaptation of neighbor influence. By modeling neighbor features as ordered sequences and leveraging LNNs' internal feedback dynamics, SL adapts feature extraction in real time based on local context and propagation history. This design offers fine-grained control over hierarchical dependencies and allows dynamic modulation of contributions from different neighborhood layers. SL is model-agnostic and can be seamlessly integrated with both classical and state-of-the-art GNNs. Extensive experiments across ten benchmark datasets show that SL consistently improves node classification accuracy and significantly alleviates over-smoothing in deep GNNs. These results highlight that order-aware and dynamically regulated propagation represents a powerful, flexible alternative to traditional multi-order aggregation, enhancing the adaptability and expressiveness of GNNs for complex graph learning tasks.

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Integral Sliding Mode control for PMSM with Uncertainties and Disturbances via Nonlinear Disturbance Observer

Integral Sliding Mode Control for PMSM with Uncertainties an...

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Chinese control Conference (CCC)

作者： Jianxiong Li Hongmei Ren Songhua Wang Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China The Key Lab of Industrial Computer Control Engineering of Yanshan University Qinhuangdao China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, a nonlinear disturbance observer-based (NDOB) integral sliding mode control (SMC) algorithm is proposed for speed control of permanent magnet synchronous motor (PMSM) in the presence of uncertain parameters and external time-varying load toque. Firstly, three nonlinear disturbance observers (NDOs) are constructed to estimate the lumped disturbances comprising of uncertain parameters and/or disturbances in speed loop and two current loops, respectively. Secondly, an integral sliding mode controller is developed based on the NDOs. It can be proved that the resulting closed loop system is asymptotically stable and the speed tracking error converges to zero asymptotically in theory. Finally, experiment results are given to validate the proposed control method.

关键词： Torque Disturbance observers Sliding mode control Uncertainty Velocity control Mathematical model Upper bound

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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A PD- Type iterative learning algorithm for networked control systems with missing data 2019

A PD- Type iterative learning algorithm for networked contro...

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2019 International Conference on Artificial Intelligence and computer Science, AICS 2019

作者： Cheng, Xiaoyan Dong, Yahui Wang, Hongbin Wang, Qinzhao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Weapon and Control Department Army Academy of Armored Forces Beijing100072 China

ISBN: (纸本)9781450371506

In this paper, a PD-type iterative learning control method is proposed for a class of nonlinear discrete networked control systems with measurement signal and control signal data dropouts. Proportional and differential items of the error signal are employed in this method to modify the current control signal, which takes full advantage of the historical error signals. The data dropout is described as a stochastic and independent Bernoulli process with a given probability. In addition, the zero-order holding method is introduced at the data receivers of the controller and actuator. Finally, the stability analysis and simulation results are performed to verify the convergence and effectiveness of the proposed method. © 2019 Association for Computing Machinery.

关键词： Networked control systems

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Improved Smith Predictive control Design of Aeroengine Based on H∞ algorithm

Improved Smith Predictive Control Design of Aeroengine Based...

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第三十九届中国控制会议

作者： Jie-Jie Li Xian Du Xi-Ming Sun Kun-Zhi Liu Yong-Feng Gao School of Control Science and Engineering Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equiment of Ministry of Education Dalian University of Technology Department of Electrical and Computer Engineering University of California Santa Barbara

Aiming at the network delay problem that exists in the aeroengine distributed control,system stability of the original Hcontroller under different time delays is investigated,and an improved Smith predictive structure is proposed to reduce the influence of ***,according to the control requirements of aeroengine under some typical operating conditions,the appropriate parameters of performance index weighting function are selected to obtain corresponding H output feedback ***,Lyapunov function is constructed to analyze the stability of the original designed system under delays,and the allowable maximum delay that ensures the system stability is determined *** order to reduce the impact of delays and enhance the system robustness,an improved Smith compensator is designed to form a composite controller on the basis of HH ***,taking a turbofan engine control as an example,some numerical simulation cases considering varying-time delay and disturbance are conducted to verify the effectiveness and robustness of the proposed method.

关键词： Aeroengine H∞control time delay stability analysis Smith predictive controller

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Lyapunov-based Singular Perturbation Results in the Framework of Hybrid Systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2027-2032页

作者： Xue-Fang Wang Kun-Zhi Liu Xi-Ming Sun Andrew R. Teel Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA

Stability properties of singularly perturbed hybrid systems are investigated via Lyapunov functions with assistance from the invariance principle. Both continuously differentiable Lyapunov functions and non-smooth Lyapunov functions are considered. In each case, under appropriate assumptions, uniform asymptotic stability and uniform global asymptotic stability are established. An estimate of the basin of attraction is given for the former property. Two examples are given to illustrate the proposed theoretical results based on continuously differentiable Lyapunov functions. In addition, one example for switched learning inclusions with unstable modes is given to show the effectiveness of the results obtained based on non-smooth Lyapunov functions.

关键词： Singular perturbation hybrid dynamical systems Lyapunov function methods uniform asymptotic stability uniform global asymptotic stability

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On the Existence of Lagrange Multipliers in Distribution Network Reconfiguration Problems

arXiv

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arXiv 2025年

作者： Liu, Rong-Peng Song, Yue Wang, Xiaozhe Zeng, Bo Department of Electrical and Computer Engineering McGill University MontrealQCH3A 0E9 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201210 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201210 China Department of Industrial Engineering Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States

Distribution network reconfiguration (DNR) is an effective approach for optimizing distribution network operation. However, the DNR problem is computationally challenging due to the mixed-integer non-convex nature. One feasible approach for addressing this challenge is to reformulate binary line on/off state variables as (continuous) non-convex constraints, leading to a nonconvex program. Unfortunately, it remains unclear whether this formulation satisfies the Karush-Kuhn-Tucker (KKT) conditions at locally optimal solutions. In this brief, we study the existence of Lagrange multipliers in DNR problems and prove that under mild assumptions, Lagrange multipliers exist for the DNR model at every locally optimal solution almost surely such that the KKT conditions hold. Copyright © 2025, The Authors. All rights reserved.

关键词： Lagrange multipliers

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An Early Fault Detection Method of the Induced Draft Fan Based on Long-Short Term Memory Network and Double Warning Thresholds

An Early Fault Detection Method of the Induced Draft Fan Bas...

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Chinese control and Decision Conference, CCDC

作者： Wei Wei You Lv Yizhuo Liu Qing Li Shisen Xu Jun Li School of Control and Computer Engineering North China Electric Power University Beijing P. R. China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing P. R. China Beijing Key Laboratory of New Technology and System on Measuring and Control for Industrial Process North China Electric Power University Beijing P. R. China Huaneng Technology Innovation Center Beijing P. R. China Xian Thermal Power Research Institute Company Limited Xian P. R. China

ISBN: (纸本)9781665478977

The induced draft (ID) fan is a significant piece of auxiliary equipment in thermal power plants. An early detection of possible faults in the ID fan may be used for effective predictive maintenance, thereby reducing the overall maintenance cost and improving the reliability and availability of thermal power plants. This paper proposed a genetic algorithm and long-short term memory network (GA-LSTM)-based condition monitoring approach using normal operation data of the ID fan. Furthermore, the LSTM network and the sliding window technique were introduced to construct double thresholds, which contains a dynamic threshold and a static threshold. The GA-LSTM-double thresholds method was applied to a coal-fired power plant to achieve early fault detection and diagnosis. The results demonstrates that the proposed method is capable of detecting minor anomalies in advance.

关键词： Fans Fault detection Neurons Predictive models Indexes Reliability State estimation

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RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight

arXiv

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arXiv 2020年

作者： Zhou, Boyu Pan, Jie Gao, Fei Shen, Shaojie Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou310007 China

—Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization (PGO) approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce a perception-aware planning strategy to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We will release our implementation as an open-source package1 for the community. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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