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7th Chinese Conference on Swarm intelligence and Cooperative control, CCSICC 2023

作者： Ding, Yu Cong, Yirui Wang, Xiangke Cheng, Long College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819733316

This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors;the other agents utilize low-accuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results indicate the effectiveness of our algorithm and verify our method can significantly improve the estimation accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Three-Dimensional Cooperative Guidance Against Aerial Target for Unpowered Glide Vehicle

Three-Dimensional Cooperative Guidance Against Aerial Target...

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Chinese Automation Congress (CAC)

作者： Bocun Li Ling Li Huaming Zhang Yichuan Zhu National Key Laboratory of Science and Technology on Aerospace Intelligence control Beijing Aerospace Automatic Control Institute Beijing China Committee of Science and Technology Beijing Aerospace Automatic Control Institute Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligence control Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper investigates the spatial-temporal guidance problem for multiple unpowered High-speed Glid Vehicles (HGV). First, a 3D vector guidance law is given by extending the traditional 2D guidance law to 3D scenario, in which the terminal angle constraint and time-coordination of multiple HGVs are satisfied. Then, the time-to-go estimation problem considering moving target for HGV without active velocity control is addressed by numerical integration, and from which predictive intercepting point (PIP) is gained, thus the problem of intercepting a moving target simultaneously is simplified as guiding multiple HGVs to hit a fixed point along different directions at same time. Taking into account that aerial target can evade threats through terminal maneuvering, an extended state observer is used to estimate the acceleration of target. Accordingly, a sliding mode terminal guidance law designed in the framework of Line-of-Sight (LOS), which is different from the previous guidance law, is used to compensate the disturbance caused by the maneuvering target. Unlike similar existing results, the proposed guidance strategy for HGV can satisfy spatial-temporal constraints when the target is moving or maneuvering, and the guidance law designed in this paper is appropriate for unpowered vehicles without constant velocity. Numerical simulations are provided to show the effectiveness of the proposed cooperative guidance law.

关键词： Three-dimensional displays Fluctuations Automation Design methodology Velocity control Line-of-sight propagation Numerical simulation Vectors State estimation

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Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement 24

Receiver-Agnostic Radio Frequency Fingerprint Identification...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Liu, Hongli Zhu, Shaoxian Yang, Liu Li, Qiang Lin, Jingran University of Electronic Science and Technology of China School of Information and Communication Engineering China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China

ISBN: (纸本)9781665496261

The Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers' fingerprints are inevitably introduced, and different receivers affect emitters' fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers' effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters' features are disentangled from the receivers', through multi-receiver's training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination. © 2023 IEEE.

关键词： Deep learning

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Hybrid D-DEPSO for Multi-objective Task Assignment in Hospital Inspection 8th

Hybrid D-DEPSO for Multi-objective Task Assignment in Hosp...

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8th International Workshop on Advanced Computational intelligence and Intelligent Informatics, IWACIII 2023

作者： Zhang, Chun Mei Ma, Xin Yao Zhai, Bin Taiyuan University of Science and Technology No.66 Waliu Road Wanbailin District Shanxi Taiyuan China Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Wanbailin District Shanxi Taiyuan China

ISBN: (纸本)9789819975891

Hospital inspection tasks include temperature measurement, disinfection, emergency treatment, etc. Inspection robots can assist people in carrying out autonomous inspections and reduce the pressure on hospital staff. In this paper, we focus on the task assignment of hospital inspection robots, i.e., assigning multiple tasks to different robots to achieve the highest level of task completion. For the task assignment model of the inspection robots, a multi-objective mathematical model of task assignment is established, considering the benefit, cost, and execution time of task assignment. For the optimization scheme, a hybrid algorithm of discrete differential evolution and particle swarm optimization (D-DEPSO) algorithm is designed, applying differential mutation operation to the population initialization process of the particle swarm optimization (PSO) algorithm to expand the diversity of the population and improve the optimization-seeking ability of the algorithm. For coordination among objectives, the adjustment method of objective weights is proposed so as to achieve balance among objectives. The experimental results show that the designed method can improve the utility of task assignment in the hospital inspection process and thus efficiently complete the hospital inspection tasks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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A characteristic modeling method of error-free compression for nonlinear systems

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control Theory and technology 2021年第3期19卷 375-383页

作者： Bin Meng Yun-Bo Zhao Jing-Jing Mu Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijingChina Department of Automation University of Science and Technology of ChinaBeijingChina China Aerospace Science and Technology Corporation BeijingChina

The existence of error when compressing nonlinear functions into the coefficients of the characteristic model is known to be a key issue in existing characteristic modeling approaches,which is solved in this work by an error-free compression *** first define a key concept of relevant states with corresponding compressing methods into their coefficients,where the coefficients are continuous and bounded and the compression is ***,we give the conditions for decoupling characteristic modeling for MIMO systems,and sequentially,we establish characteristic models for nonlinear systems with minimum phase and relative order two as well as the flexible spacecrafts,realizing the equivalence in the characteristic model ***,we explicitly explain the reasons for normalization in the characteristic model theory.

关键词： Characteristic modeling Relevant states Error-free compression Flexible spacecraft Normalization

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INN: A Deep Correction Network for High-Dynamic Inertial Navigation 4

INN: A Deep Correction Network for High-Dynamic Inertial Nav...

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4th International Conference on Industrial Automation, Robotics and control Engineering, IARCE 2024

作者： Gu, Tao Cong, Li Yang, Jing Guo, Yuzhu Wang, Lina School of Automation Science and Electrical Engineering Beihang University Beijing China School of Electronic Information Beihang University Engineering dept. Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China

ISBN: (纸本)9798350380323

Inertial navigation has gained extensive application because it solely relies on built-in Inertial Measurement Unit (IMU) devices. Nevertheless, due to its inherent accumulative integral error, inertial navigation is often integrated with other external devices in practical situations, which unfortunately leads to unavoidable privacy breaches. To address these issues, an Inertial Navigation Neural Network (INN) is designed to generate high-precision position estimations without relying on any other auxiliary information. The INN model consists of attitude correction and velocity regression modules. The attitude module employs a convolutional network to correct the attitude information, and the obtained rotation matrices are then fed into a recurrent neural network to regress incremental velocities and produce high-precision positions. The proposed model is validated on the simulated trajectories based on the Precise Strapdown Inertial Navigation System (PSINS) toolbox. Experimental studies demonstrated that the proposed method has a significant corrective effect on high-dynamic strapdown inertial navigation settings. The accumulated position errors are an order of magnitude smaller than those of traditional track reckoning algorithms and achieved a comparable performance with integrated navigation. © 2024 IEEE.

关键词： Inertial navigation systems

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Energy-aware Integrated Scheduling for Container Terminals with Conflict-free AGVs

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Journal of Systems science and Systems Engineering 2023年第4期32卷 413-443页

作者： Zhaolin Zhong Yiyun Guo Jihui Zhang Shengxiang Yang Institute of Complexity Science School of AutomationQingdao UniversityQingdao266071China Shandong Key Laboratory of Industrial Control Technology Qingdao266071China Qingdao Port International Company Ltd.Qingdao266011China Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityLeicesterLE19BHUK

For automated container terminals,the effective integrated scheduling of different kinds of equipment such as quay cranes(QCs),automated guided vehicles(AGVs),and yard cranes(YCs)is of great significance in reducing energy consumption and achieving sustainable *** at the joint scheduling of AGVs and YCs with consideration of conflict-free path planning for AGVs as well as capacity constraints on AGV-mate which is also called buffer bracket in blocks,a mixed integer programming model is established to minimize the energy consumption of AGVs and YCs for the given loading/unloading task.A solution method based on a novel bi-level genetic algorithm(BGA),in which the outer and the inner layer search the optimal dispatching strategy for QCs and YCs,respectively,is *** validity of the model and the algorithm is verified by simulation experiments,which take the Port of Qingdao as an example and the performance under different conflicting resolution strategies is *** results show that,for the given task,the proposed solution to conflict-free path and the schedule provided by the algorithm can complete the task with minimum energy consumption without loss of AGVs utilization,and the number of AGV-mates should be adjusted according to the task rather than keeping *** results indicate that our proposed approach could efficiently find solutions within 6%optimality *** consumption is dropped by an average of 15%.

关键词： Automated container terminal conflict-free routing energy saving improved genetic algorithm

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Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1822-1833页

作者： Wei Xu Chen Zhao Jie Cheng Yin Wang Yiqing Tang Tao Zhang Zhiming Yuan Yisheng Lv Fei-Yue Wang Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Signal&Communication Research Institute China Academy Railway SciencesBeijing 100081China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.

关键词： High-speed railway reinforcement learning train timetable rescheduling Transformer

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A Co-Located Mobile Collaborative Augmented Reality for Shared Experiences 2

A Co-Located Mobile Collaborative Augmented Reality for Shar...

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2nd International Conference on Artificial intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Ding, Ji Fang, Wei Beijing Aerospace Automatic Control Institute Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China School of Automation Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350360363

Augmented reality (AR) has increasingly been used in varying activities by superimposing visual instructions on the workbench directly. Nevertheless, most current AR deployment is mainly first-person perspective, and it is difficult for colleagues to observe the other's operating intent directly when involving co-located users in complex environments, such as collaborative AR assembly. To this end, this paper tries to propose a co-located alignment-enabled mobile collaborative AR system. Instead of the commercial AR glass, we first establish a self- contained visual-inertial odometry system for mobile hand- held devices, and it can realize markerless pose estimation for mobile objects and enable the foundation for easy-to-deploy applications. Then, a collaborative alignment framework from multi co-located AR clients is proposed, and thus the unified global coordinate system is available for a shared immersive AR workplace on the shop floor. Finally, extensive experiments are carried out on the motion tracking and collaborative AR experience, and experimental results illustrate the effectiveness and feasibility of the proposed system. © 2023 IEEE.

关键词： Augmented reality

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Discrete Adaptive control of Hypersonic Vehicles Based on Characteristic Models with Fuzzy Neural Network Observer

Discrete Adaptive Control of Hypersonic Vehicles Based on Ch...

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Chinese control and Decision Conference, CCDC

作者： Yafei Chang Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

A novel discrete adaptive control scheme is developed based on the characteristic modeling theory, for the attitude control of a hypersonic vehicle with strong nonlinearities and uncertainties. A new structure of the characteristic model is proposed firstly, in which an extended state is added to describe the unmolded dynamics and disturbance. Then a fuzzy neural network observer is constructed to approximate the extended state, and the updated law of the characteristic parameters and the net parameters are designed simultaneously based on the structure of the characteristic model. Further, to reject the undesired influence of the time-variation of characteristic parameters and the approximation error of the neural network, a nonlinear term containing a saturation function is added to the parameter adaption law. Then an error-surface based control law is proposed on the basis of the characteristic model. Using the Lyapunov stability theory, the closed-loop system is proved stable and convergence properties of the system are assured. Finally, the effectiveness of the proposed adaptive control scheme is illustrated by several representative simulations.

关键词： Adaptation models Fuzzy control Uncertainty Neural networks Observers Fuzzy neural networks Robustness

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