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Adaptive fuzzy asymptotic control design for MIMO nonlinear systems with state constraints

Journal of control and Decision 2023年第4期10卷 610-623页

作者： Yongchao Liu Bowen Zeng Qidan Zhu Lipeng Wang Institute of Complexity Science School of AutomationQingdao UniversityQingdaoPeople’s Republic of China Shandong Key Laboratory of Industrial Control Technology QingdaoPeople’s Republic of China College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbinPeople’s Republic of China

This paper addresses the asymptotic control problem of uncertain multi-input and multi-output(MIMO)nonlinear *** considered MIMO systems contain unknown virtual control coefficients(UVCCs)and state *** Lyapunov function by associating with the lower bounds of UVCCs is presented to counteract the adverse effect deriving from *** state constraints are ensured by utilising the barrier Lyapunov ***,the asymptotic tracking controller is recursively constructed by combining the backstepping technique with fuzzy logic *** remarkable character of the designed controller is that the asymptotic tracking performance can be achieved by introducing some smooth functions into adaptive backstepping *** contrast to the existing results,the conditions on the UVCCs are ***,the new control design is illustrated by a practical example.

关键词： Asymptotic tracking control state constraints bound estimation method nonlinear systems

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OmniRace: 6D Hand Pose Estimation for Intuitive Guidance of Racing Drone

OmniRace: 6D Hand Pose Estimation for Intuitive Guidance of ...

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

作者： Valerii Serpiva Aleksey Fedoseev Sausar Karaf Ali Alridha Abdulkarim Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Moscow Moscow Russia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents the OmniRace approach to controlling a racing drone with 6-degree of freedom (DoF) hand pose estimation and gesture recognition. To our knowledge, this is the first technology enabling low-level control of high-speed drones through gestures. OmniRace employs a gesture interface based on computer vision and a deep neural network to estimate 6-DoF hand pose. The advanced machine learning algorithm robustly interprets human gestures, allowing users to control drone motion intuitively. Real-time control tests validate the system’s effectiveness and its potential to revolutionize drone racing and other applications. Experimental results conducted in simulation environment revealed that OmniRace allows the users to complite the UAV race track significantly (by 25.1%) faster and to decrease the length of the test drone path (from 102.9 to 83.7 m). Users preferred the gesture interface for attractiveness (1.57 UEQ score), hedonic quality (1.56 UEQ score), and lower perceived temporal demand (32.0 score in NASA-TLX), while noting the high efficiency (0.75 UEQ score) and low physical demand (19.0 score in NASA-TLX) of the baseline remote controller. The deep neural network attains an average accuracy of 99.75% when applied to both normalized datasets and raw datasets. OmniRace can potentially change the way humans interact with and navigate racing drones in dynamic and complex environments. The source code is available at https://***/SerValera/***.

关键词： Hands Machine learning algorithms Tracking Navigation Source coding Pose estimation Artificial neural networks Real-time systems intelligent robots Drones

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TornadoDrone: Bio-Inspired DRL-Based Drone Landing on 6D Platform with Wind Force Disturbances

TornadoDrone: Bio-Inspired DRL-Based Drone Landing on 6D Pla...

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IEEE International Conference on Robotics and Biomimetics

作者： Robinroy Peter Lavanya Ratnabala Demetros Aschu Aleksey Fedoseev Dzmitry Tsetserukou Intelligent Space Robotics Laboratory CDE Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Autonomous drone navigation faces a critical challenge in achieving accurate landings on dynamic platforms, especially under unpredictable conditions such as wind turbulence. Our research introduces TornadoDrone, a novel Deep Reinforcement Learning (DRL) model that adopts bio-inspired mechanisms to adapt to wind forces, mirroring the natural adaptability seen in birds. This model, unlike traditional approaches, derives its adaptability from indirect cues such as changes in position and velocity rather than direct wind force measurements. TornadoDrone was rigorously trained in the gym-pybullet-drone simulator, which closely replicates the complexities of wind dynamics in the real world. Through extensive testing with Crazyflie 2.1 drones in both simulated and real windy conditions, TornadoDrone demonstrated high performance in maintaining high-precision landing accuracy on moving platforms, surpassing conventional control methods such as PID controllers with Extended Kalman Filters. The study not only highlights the potential of DRL to tackle complex aerodynamic challenges but also paves the way for advanced autonomous systems that can adapt to environmental changes in real time. The success of TornadoDrone signifies a leap forward in drone technology, particularly for critical applications such as surveillance and emergency response, where reliability and precision are paramount.

关键词： Wind Adaptation models Accuracy Biological system modeling Surveillance Aerodynamics Reliability Robots Drones Testing

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Privacy - Preserving Event - Triggered Predefined Time Containment control for Networked Agent Systems 14

Privacy - Preserving Event - Triggered Predefined Time Conta...

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14th International Conference on Information science and technology, ICIST 2024

作者： Li, Weihao Yue, Jiangfeng Shi, Mengji Lin, Boxian Qin, Kaiyu School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China National Laboratory on Adaptive Optics Chengdu610209 China

ISBN: (纸本)9798350353334

This paper addresses the privacy-preserving event-triggered predefined time containment control problem for net-worked agent systems. A novel containment control scheme is developed that integrates privacy protection with event-triggered mechanisms, optimizing network efficiency b y minimizing un-necessary data transmission while ensuring robust containment within a specified time frame. T he proposed control scheme ensures the confidentiality of agents' information through output masking, thereby maintaining both privacy and control accuracy. Furthermore, it provides a distinct advantage over traditional finite-time and fi xed-time co ntrol me thods by gu aranteeing con-vergence to the desired state within a predefined time, regardless of initial conditions. Finally, some simulation results are given to verify the effectiveness of the proposed containment control scheme. © 2024 IEEE.

关键词： Differential privacy

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Effect of IQ Imbalance on Cooperative Jamming Cancellation for 6G Security in Physical Layer

Effect of IQ Imbalance on Cooperative Jamming Cancellation f...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Jin, Mingyue Guo, Wenbo Zhao, Hongzhi Shao, Shihai University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100191 China

ISBN: (纸本)9798350368369

While 6G provides communication services for massive wireless devices, it faces huge security risks due to its open feature. Cooperative jamming techniques in physical layer security provide a viable way to ensure 6G security. By actively transmitting jamming signals to deteriorate the eavesdropping channel, while authorized receivers implement cooperative jamming cancellation (CJC) using the prior knowledge of cooperative jamming, thus securing the communication at the physical layer. In practice, incomplete matching of oscillator device characteristics will lead to the in-phase and quadrature (IQ) imbalance problem, where the quadrature branches of the up and down converted carrier signals are not perfectly orthogonal to the in-phase branches, which may degrade the CJC performance. For this reason, this paper specifically analyzes the impact of the IQ imbalance problem on the CJC performance, where a closed-form expression of the jamming cancellation ratio (JCR) with respect to amplitude imbalance and phase imbalance is derived. The simulation results show that when jamming-to-noise ratio (JNR) is 35 dB, the system can tolerate up to 1% amplitude imbalance and 1° phase imbalance with 3 dB JCR degradation. Moreover, the IQ imbalance has a noticeable effect on the reachable security rate of the communication system. © 2025 IEEE.

关键词： Telecommunication services

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Effect of Frequency Offset on Collaborative Beamforming of UAV Swarm in space-Air-Ground Integrated Networks

Effect of Frequency Offset on Collaborative Beamforming of U...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Du, Jiaxin Guo, Wenbo Yan, Mu Zhao, Hongzhi Shao, Shihai University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100191 China

ISBN: (纸本)9798350368369

The space-air-ground integrated networks (SAGNs) are promising components of the next generation communication system, and unmanned aerial vehicle (UAV) swarms are the primary infrastructures of the SAGNs aerial layer, used for relaying information. Furthermore, UAV swarms form virtual antenna arrays (VAAs) and implement collaborative beamforming (CB), which can improve the deep-space communication distance and expand the ground coverage. However, in practical engineering, due to the doppler effect and local oscillator random frequency drift, there is a frequency offset between UAVs, even after frequency synchronization, resulting in CB performance degradation. Therefore, in the presence of frequency offset, this paper investigates and presents the derivation of the average far-field beampattern, 3-dB beamwidth, and complementary cumulative distribution function (CCDF) for UAV swarm executing CB in SAGNs. The results show that the average far-field beam-pattern, peak power, 3-dB beamwidth, and CCDF, deteriorate as frequency offset intensifies. Furthermore, as the UAV swarm scale increases, the peak power attenuation is aggravated, and the decrease in CCDF is steeper. With the increase in the UAV swarm distribution range, the 3-dB beamwidth expansion is alleviated. © 2025 IEEE.

关键词： Beamforming

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A Method for Marine Ship Formation Intention Recognition based on BiConvLSTM-Attention 3

A Method for Marine Ship Formation Intention Recognition bas...

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3rd International Conference on Electronic Information Engineering and Computer science, EIECS 2023

作者： Xi, Yunqiao Zhang, Junping Liu, Jian Wang, Linlin Xu, Hongfeng Chen, Linxiu Jiang, Jixiang Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Institute of Electrical and Mechanical Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350316674

To solve the problem that current dynamic intention recognition methods fail to make full use of time domain variation information between multi-temporal group targets, which leads to low accuracy of intention recognition. This paper proposes a bidirectional convolutional long short term memory-attention network for marine formation target intention recognition. The network takes the multi-source trajectory data of the marine ship formation target as the input, extracts and uses the target change rule and time domain characteristics of the Marine formation data, and trains the model to have the ability to independently learn the weight of information in different time periods. The simulation results show that the method has good performance and can meet the needs of practical application. © 2023 IEEE.

关键词： intention recognition long short-term marinship formation temporal features

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Impact of aerosol-radiation interaction and heterogeneous chemistry on the winter decreasing PM_(2.5)and increasing O_(3)in Eastern China 2014–2020

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Journal of Environmental sciences 2025年第5期151卷 469-483页

作者： Yasong Li Tijian Wang Qin’geng Wang Mengmeng Li Yawei Qu Hao Wu Min Xie School of the Environment Nanjing UniversityNanjing 210023China School of Atmospheric Sciences Nanjing UniversityNanjing 210023China College of Intelligent Science and Control Engineering Jinling Institute of TechnologyNanjing 211169China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041China School of Environment Nanjing Normal UniversityNanjing 210023China

In the context of the prevalent winter air quality issues in China marked by declining PM_(2.5)and rising O_(3),this study employed amodified WRF-Chem model to examine the aerosol radiation interaction(ARI),heterogeneous chemistry(AHC),and their combined impact(ALL)on the variations in O_(3)and PM_(2.5)during the 2014–2020 in eastern *** analysis confirmed that ARI curtailed O_(3)while elevating PM_(2.5).AHC reduced O_(3)through heterogeneous absorption of NOx and hydroxideswhile notably fostering fine-grained sulfate,resulting in a PM_(2.5)*** reductions mitigated the inhibitory impact of ARI on meteorological fields and photolysis *** reduction individually without aerosol feedback led to a 5.43 ppb O_(3)increase and a 22.89μg/m^(3)PM_(2.5)*** and AHC amplified the emission-reduction-induced(ERI)O_(3)rise by 1.83 and 0.31 ppb,*** response of ARI to emission diminution brought about a modest PM_(2.5)increase of 0.31μg/m^(3).Conversely,AHC,acting as the primary contributor,caused a noteworthy PM_(2.5)decrease of 4.60μg/m^(3).As efforts concentrate on reducing PM_(2.5),the promotion of ARI on PM_(2.5)counterbalanced the efficacy of emission reduction and the AHC-induced strengthening of PM_(2.5)*** ALL magnified the ERI O_(3)increase by 38.9%and PM_(2.5)decrease by 18.7%.Sensitivity experiments with different degrees of emission reduction demonstrated a consistent linear relationship between the ALL-induced enhancement of O_(3)increase and PM_(2.5)decrease to the ERI PM_(2.5)*** investigation revealed the complex connection between emissions and aerosol feedback in influencing air quality.

关键词： Aerosol-radiation interaction Heterogeneous chemistry O_(3)increase PM_(2.5)decrease Emission reduction

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Fair Incentive Allocation for Secure Data Sharing in Blockchain Systems: A Shapley Value Based Mechanism 3rd

Fair Incentive Allocation for Secure Data Sharing in Block...

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Yang, Ming Feng, Honglin Wang, Xin Jiao, Xuguo Wu, Xiaoming Qin, Shuwei Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Public Security of Shandong Province Shandong Intelligent Public Security Joint Innovation Center Jinan China

ISBN: (纸本)9789819710829

In the contemporary era of information technology, the exponential surge in data has rendered colossal potential value, which would be fully unlocked through data circulation and sharing. However, when the data contains sensitive information, special attention must be paid to the data privacy and security problem for data sharing. A feasible solution is to incorporate blockchain technology into the data sharing system, and the new challenge is how to provide fair rewards for data owners to motivate them to contribute data. To address this challenge, in this paper, we first design a blockchain-based data sharing system to enhance the trust and reliability of the data shared among participants. Then we propose an incentive allocation method based on the Shapley value, which comprehensively incorporates each participant’s contribution while ensuring fairness for the allocation. Finally, multiple numerical simulations are conducted to validate the effectiveness of the proposed mechanism. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Blockchain

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Multi-view and Multi-target Association Algorithm for Unmanned Aerial Vehicle Clusters Based on Siamese Network

Multi-view and Multi-target Association Algorithm for Unmann...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Zhang, Hang Song, Chuang Zheng, JiangBin Hao, MingRui School of Software Northwestern Polytechnical University Shaanxi Xi’an710072 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789811966125

The multi-target association algorithm of unmanned aerial vehicles (UAV) clusters in unknown environments is used to solve the problem of multiple UAVs, which can correctly determine whether the data from different perspectives contain the same target and eliminate redundant targets. This problem is different from that of multi-target tracking under the same perspective. However, the inherent relationship and differences of data from various perspectives of UAV clusters contribute to the problem of inaccurate target positioning information caused by low-cost detectors and inertial measurement units of UAVs and the constraints of limited communication capability between UAV clusters. Thus, using multi-perspective data is the key to improving the accuracy of multi-perspective situational awareness. This paper proposes a multi-view and multi-target association algorithm for UAV clusters based on the Siamese network. This algorithm can realise the problem of multi-view and multi-target identity judgement with high accuracy, achieve the requirements of high accuracy and high generalisation performance with the depth model based on convolution neural networks, extract the depth features from the optical image and map the original data to the depth feature space. Thus, the same target is matched by comparing the distance measurement of multi-view data in the feature space. The algorithm is verified by an aircraft cluster flight test, which proves the feasibility and accuracy of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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