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Controllability of heterogeneous networked sampled-data systems

Science China(Information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

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Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-dendrite Spiking Neuron and Dynamic Thresholds

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 618-629页

作者： Xingyue Liang Qiaoyun Wu Yun Zhou Chunyu Tan Hongfu Yin Changyin Sun the School of Artificial Intelligence Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education IEEE the Institute of Artificial Intelligence Hefei Comprehensive National Science Center the School of Automation Southeast University the Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education

Deep reinforcement learning(DRL) achieves success through the representational capabilities of deep neural networks(DNNs). Compared to DNNs, spiking neural networks(SNNs),known for their binary spike information processing, exhibit more biological characteristics. However, the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains, directly related to the information integration and transmission in *** by the advanced computational power of dendrites in biological neurons, we propose a multi-dendrite spiking neuron(MDSN) model based on Multi-compartment spiking neurons(MCN), expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane *** apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decisionmaking tasks. The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources. Our model uses a bioinspired event-enhanced dendrite structure to emphasize features. Meanwhile, by utilizing dynamic membrane potential thresholds, it adaptively maintains the homeostasis of MDSN. Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.

关键词： Deep reinforcement learning multi-compartment spiking neurons spiking neural network

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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A Seamless Pedestrian Localization system Based on GNSS/IμUWB/MAP Integration

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IEEE Internet of Things Journal 2025年第11期12卷 16653-16667页

作者： Zhang, Shuning Tang, Hongjing Chen, Liangming Gao, Yufeng Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Pedestrian localization has broad applications. However, achieving seamless positioning in complex environments remains a significant challenge due to factors like environmental occlusion and indoor-outdoor transitions. This paper proposes a combined localization framework using Global Navigation Satellite system (GNSS), Inertial Measurement Unit (IMU), Ultra-Wideband (UWB), and MAP data to address this issue. In the framework, a three-step GNSS localization method is proposed to enhance accuracy and stability by eliminating Non-Line-of-Sight (NLOS) effects and filtering anomalies. Inertial Navigation system (INS) and Pedestrian Dead Reckoning (PDR) models are integrated to improve local precision and stability. UWB, known for its superior localization capabilities, has been integrated into some smartphones and is promising for large-scale indoor applications. The paper presents a gradient descent-based optimal anchor deployment algorithm and localization methods for varying anchor quantities. Additionally, a non-stationary Markov model fuses map data for trajectory prediction and correction. Finally, anomaly detection filtering and low-pass filtering are applied to the corrected trajectories, yielding high accuracy and stability. Experiments on the Honor V20 and iQOO U3 smartphones showed significant improvements compared to the baseline algorithm. Outdoor Root Mean Square Error (RMSE) was reduced by 44.4% (10.83m) for the Honor V20 and 40.0% (10.77m) for the iQOO U3. Additionally, outdoor Standard Deviation (STD) improved by 43.9% (4.80m) and 43.5% (5.50m), respectively. In transitional areas, RMSE improved by 4.61% (10.77m) and 19.6% (4.76m), respectively. Indoor RMSE also showed improvements, with the Honor V20 reducing it by 8.00% (3.79m) and the iQOO U3 by 5.78% (3.91m). © 2025 IEEE.

关键词： Location awareness Global navigation satellite system Accuracy Pedestrians Filtering Sensors Prediction algorithms Predictive models Smart phones Kalman filters

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High-Misalignment Tolerance WPT system for UAV by Symmetric Rotating Magnetic Field 26

High-Misalignment Tolerance WPT System for UAV by Symmetric ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fengshuo Lan, Yu Zhang, Qingxuan Xiong, Mingxing Jiang, Jinhai Song, Kai Wang, Xudong Xu, Wanli School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of System Engineering Academy of Military Science Beijing China

ISBN: (纸本)9798331521950

This paper proposes an novel solenoid magnetic coupler for omnidirectional wireless charging of unmanned aerial vehicles (UAVs). The magnetic coupler features asymmetry compensation, a compact structure, high rotational and misalignment tolerance, and low magnetic leakage interference. The transmitting coils consist of three groups of solenoid coils, each comprising a large coil and a small coil connected in series. The small coil compensates for the uneven coupling caused by axial misalignment of the UAV. The excitation current phase difference of 120° in each transmitter coil group generates rotating magnetic field. The transmitting coils primarily produce horizontal magnetic fields with minimal vertical components, significantly reducing electromagnetic interference with UAV equipment. Two groups of receiving solenoid coils are conformally installed on the UAV landing gear. The topology, where each group of receiving coils is rectified and then connected in series, compensates for the uneven coupling caused by radial misalignment. Simulation results indicate that the system maintains a stable received power of 379W-477W under angular offsets of 0-360° and positional offsets of ±60mm, demonstrating excellent misalignment tolerance. © 2025 IEEE.

关键词： Drones

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Study on Multi-Motor Synchronization Control for Nuclear Island Main Equipment Transporter Based on Improved Virtual Line-Shafting Structure 11th

Study on Multi-Motor Synchronization Control for Nuclear Isl...

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11th Frontier Academic Forum of Electrical engineering, FAFEE 2024

作者： Sheng, Beining Song, Beibei An, Yanbo Du, Hua Cui, Shumei School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

ISBN: (纸本)9789819788231

The existing drive method of the main equipment transporter of the nuclear island makes the transporter have frequent jamming problems when the transporter going straight and turning, which affects the safety during the transporter of the main equipment. Therefore, it is necessary to design an on-board multi-motor synchronous system with differential synchronization function. Although the traditional virtual line-shafting structure has the ability of same-speed synchronization and differential synchronization, there is a large synchronization error. For this reason, this paper proposes a multi-motor synchronous control method based on the improved virtual line-shafting structure, which can effectively solve the inherent synchronous delay problem of the virtual line-shafting. First, analyze and research the combination method of virtual line-shafting structure and deviation coupling structure, and designs the electronic differential to be able to adapt to the requirements of same-speed and differential synchronization. Then the influence of virtual line-shafting parameters on the system is analyzed using the root locus method, and a simplified parameter model of the virtual line-shafting is established, on the basis of which an improved virtual line-shafting control method with low synchronous delay is proposed. The results show that this method can significantly reduce the synchronization error compared with the traditional virtual line-shafting. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Root loci

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Cataract Surgical Instruments Segmentation Free of Motion Blur Influence 24

Cataract Surgical Instruments Segmentation Free of Motion Bl...

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13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024

作者： Sun, Xuebin Wang, Jinxi Yang, Xiaofei School of Electrical Automation and Information Engineering Tianjin University Tianjin China Department of Aerospace Laser Technology and System Shanghai Institute of Optics and Fine Mechanics Shanghai China

ISBN: (纸本)9798400717956

Most of the existing cataract surgical instrument segmentation algorithms fail to consider the motion blur caused by the surgeon's hand movement or defocused camera. Most prior deblurring methods are oriented to visual tasks to merely recover the structure information from motion-blurred images. However, during surgical instrument segmentation, it is important to obtain the exact location of the surgical instruments at the current time to pave the way for subsequent downstream tasks, such as tracking and position estimation. In this work, we propose a spatiotemporal (ST) information-based network, consisting of the deblurring module and a subsequent segmentation module, for cataract surgical instrument segmentation free of motion blur influence to address the missing gap in the existing literature. The ST recurrent network with a series of ST-LSTM and gradient highway units removes the motion blur, which then enables for accurate semantic segmentation. The ST network model subsequently captures the temporal and spatial correlation for semantic segmentation. Experimental results quantitatively and qualitatively demonstrate that our method outperforms the state-of-the-art on our self-constructed dataset and the CaDIS benchmark dataset. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantic Segmentation

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The prediction method for assembly poses deviation of satellite deployable arm considering force measurement uncertainty 3

The prediction method for assembly poses deviation of satell...

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2024 3rd International Conference on Aerospace and Control engineering, ICoACE 2024

作者： Lyu, Yulong Li, Dongsheng Liu, Peng Zhai, Yunong Wen, Ke School of Mechanical Engineering and Automation Beihang University Beijing100191 China Beijing Institute of Electronic System Engineering Beijing100854 China Beijing Spacecrafts China Academy of Space Technology Beijing100094 China

Proper alignment between the rod flange and the joint flange of a satellite deployable arm is crucial for ensuring high assembly accuracy. Once these flanges are mated, the constrained positions generate assembly forces that cause rebound deformation of the arm, ultimately leading to pose deviation at the joint flange surfaces at both ends of the deployable arm. During assembly, multiple iterations of mounting, dismounting, and inspection are typically required, which is time-consuming and labor-intensive, thereby limiting assembly efficiency. To address this issue, a prediction method for assembly pose deviation in satellite deployable arms is proposed. By pre-evaluating the rebound pose deviation based on assembly forces, the method significantly reduces the need for repeated physical measurements and improves overall process efficiency. First, a finite element simulation model for assembly rebound is established. Subsequently, samples are obtained using Latin Hypercube Sampling (LHS). A stochastic Kriging model (SKM) is employed to construct a predictive model that relates assembly forces to pose deviation, taking force measurement uncertainty into account. Simulation experiments confirm the effectiveness of the proposed method, demonstrating that the average relative error remains below 1.7%. The high prediction accuracy verifies the feasibility and effectiveness of this approach. © 2025 institute of Physics Publishing. All rights reserved.

关键词： Force measurement

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Enhancing Online Load Model Parameter Identification with Deep Reinforcement Learning: A DDPG-Based Approach

Enhancing Online Load Model Parameter Identification with De...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Li, Zihao Liu, Xingwei Huang, Yupeng Hu, Jianxiong Tang, Yi School of Electrical Engineering Southeast University Nanjing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology Beijing China China Electric Power Research Institute Beijing China

ISBN: (纸本)9789819620791

To cope with the complex dynamic characteristics brought about by the sharp increase in penetration of various power electronic devices, online dynamic security analysis in modern power systems has put forward higher requirements for the accuracy of simulation models. Traditional methods for load model parameter identification (PI) struggle with model complexity, computational pressure, and low generalization, which can hinder their effectiveness in online applications. To overcome these challenges, this paper proposes a novel online load model PI method utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm. Initially, a composite load model incorporating various distributed energy resources (DERs) is developed as the model basis. Subsequently, a DDPG-based framework for online load model PI is designed, featuring meticulously crafted interface functions to improve both convergence and generalization capabilities. Case studies on the EPRI-36 buses test system demonstrate the proposed method's efficiency and accuracy compared to traditional heuristic optimization methods. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep reinforcement learning

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