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Distributed Fault-Tolerant Formation Game for Quadrotor UAVs With Hybrid Attacks

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Rui-Ming Yao, Xiang-Yu Wang, Leimin Park, Ju H. Ge, Ming-Feng China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China China University of Geosciences School of Automation Wuhan430074 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of

This manuscript studies the distributed faulttolerant formation for quadrotor unmanned aerial vehicles (QUAVs) subject to actuator failures and hybrid cyber attacks via a game-theoretical approach. The desired formation, which aligns with optimal self-interests, is achieved by seeking the Nash equilibrium (NE) of the game under a strongly connected directed graph. First, a secure fault-tolerant NE seeking algorithm is presented to handle actuator failures and false data injection (FDI) attacks, with the false data being measured through extended state observers (ESOs). The proposed algorithm is established on pseudo-gradient dynamics, in which a local estimate law is used to anticipate the actions of other QUAVs. Then, an ESOsbased algorithm is developed to mitigate the influence of FDI and denial-of-service (DoS) attacks, guaranteeing that all QUAVs exponentially converge to an adjustable small domain of NE by Lyapunov stability analysis. This work is distinct from existing studies by addressing the practical scenario, where physical failures and hybrid cyber attacks occur simultaneously in the game framework of QUAVs. Eventually, numerical experiments are conducted to demonstrate the efficacy of theoretical findings. © 1965-2011 IEEE.

关键词： Drones

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Cloud-assisted nonlinear model predictive control for finite-duration tasks

arXiv

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arXiv 2021年

作者： Li, Nan Zhang, Kaixiang Li, Zhaojian Srivastava, Vaibhav Yin, Xiang The Department of Aerospace Engineering University of Michigan Ann ArborMI48109 United States The Department of Mechanical Engineering Michigan State University East LansingMI48824 United States The Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States The Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback. Unlike traditional cloud-based control that treats the cloud as powerful, remote, and sole controller in a networked-system control setting, the proposed framework aims at seamlessly integrating the two controllers for enhanced performance. In particular, we formalize the fusion problem for finite-duration tasks by explicitly considering model mismatches and errors due to request-response communication delays. We analyze stability-like properties of the proposed cloud-assisted MPC framework and establish approaches to robustly handling constraints within this framework in spite of plant-model mismatch and disturbances. A fusion scheme is then developed to enhance control performance while satisfying stability-like conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

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Semantic-Based Road Segmentation for High-Definition Map Construction 5th

Semantic-Based Road Segmentation for High-Definition Map Con...

引用

5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Zhuang, Hanyang Wang, Chunxiang Qian, Yuhan Yang, Ming University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Guangxi Key Laboratory of Automobile Components and Vehicle Technology Guangxi University of Science and Technology Liuzhou545006 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811623356

The development of autonomous driving technology proposes higher requirements of the fidelity of the high-definition maps (HD maps). The construction of HD map based on orthophotos generated from panoramic images is the state-of-the-art approach. However, in this process, the dynamic obstacles and shadows on the road captured by the panoramic camera has significant impact on reducing the map quality. Moreover, the GNSS signal may inevitably unavailable or have jitter error, leading to the unsatisfactory of the orthophoto mosaic. Therefore, an approach is proposed to tackle these problems of HD map construction. The semantic segmentation of the panoramic images is firstly implemented to extract the dynamic obstacles such as vehicles and the road segments. Then the shadows on the road segments are removed through GAN networks to generate clean orthophotos. Afterwards, the clean orthophotos are used for feature extraction and image registration based on the road segmentation to provide finer pose estimations. Finally, the GNSS data, odometer data, and estimated poses are combined to optimize the vehicle pose for orthophoto mosaic. The experimental results illustrate that the proposed approach can improve the HD map construction accuracy under the congested environment. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image registration

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A Safe Hierarchical control Scheme Incorporated with Constrained Deep Neural Networks

A Safe Hierarchical Control Scheme Incorporated with Constra...

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第三十九届中国控制会议

作者： Hongyuan Wang Jingcheng Wang Haotian Xu Shangwei Zhao Xiaocheng Li Yuanhao Shi Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai School of Electrical and Control Engineering North University of China

A methodology is proposed for the control design of hierarchical systems. The high layer makes a decision without knowing the overall systems dynamics precisely. This procedure is implemented by a deep neural network constructed ***, to handle the safe learning issue, an additional technique is developed to guarantee constraints satisfaction of the trained neural network. At the underlying layer, local model predictive control（MPC） controllers are designed to regulate the subsystems to follow the optimal trajectories derived from high layer. The sample data from lower layer is utilized as the training set of neural network in high layer. The efficacy of the presented algorithm is illustrated in a simulation example.

关键词： Hierarchical multilevel and multilayer control Energy Management Model Predictive control Deep Neural Network Safe Learning

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

Robotic Electrospinning Actuated by Non-Circular Joint Conti...

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2021 IEEE International Conference on Robotics and automation, ICRA 2021

作者： Wu, Zicong Lou, Chuqian Jin, Zhu Huang, Shaoping Liu, Ning Zou, Yun Kovac, Mirko Gao, Anzhu Yang, Guang-Zhong The Institute of Medical Robotics Department of Bioengineering Shanghai Jiao Tong University Shanghai200240 China Imperial College London Empa-Swiss Federal Laboratories for Materials Science and Technology United Kingdom Limited 999077 Hong Kong Aerial Robotics Laboratory Imperial College London LondonSW7 2AZ United Kingdom The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781728190778

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments. © 2021 IEEE

关键词： Electrospinning

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Predicting the Robustness of Undirected Network controllability

Predicting the Robustness of Undirected Network Controllabil...

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第三十九届中国控制会议

作者： Yang Lou Yaodong He Lin Wang Kim Fung Tsang Guanrong Chen Department of Electrical Engineering City University of Hong Kong Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education

Robustness of the network controllability reflects how well a networked system can maintain its controllability against destructive attacks. The measure of the network controllability robustness is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a convolutional neural network. This approach is motivated by the following observations: 1) there is no clear correlation between the topological features and the controllability robustness of a general undirected network, 2) the adjacency matrix of a network can be represented as a gray-scale image, 3) the convolutional neural network technique has proved successful in image processing without human intervention. In the new framework, preprocessing and filtering are embedded, and a sufficiently large number of training datasets generated by simulations are used to train several convolutional neural networks for classification and prediction,respectively. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting the controllability robustness of undirected networks is more accurate and reliable than the conventional single convolutional neural network predictor.

关键词： Complex network Convolutional neural network controllability Robustness Performance prediction

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Interdependence between individual and social learning shapes cooperation with asymmetric exploration

arXiv

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arXiv 2025年

作者： Hou, Zhihao She, Zhikun Liang, Quanyi Su, Qi Li, Daqing School of Mathematical Sciences Beihang University Beijing100191 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Reliability and Systems Engineering Beihang University Beijing100083 China

Cooperation on social networks is crucial for understanding human survival and development. Although network structure has been found to significantly influence cooperation, some human experiments indicate that it cannot fully explain the evolutionary patterns of cooperation. While evidence suggests that this gap arises from human exploration, our understanding of its impact mechanisms and characteristics, such as asymmetry, remains limited. Here, we seek to formalize human exploration as an individual learning process involving trial and reflection, and integrate social learning to examine how their interdependence shapes cooperation. We find that individual learning can alter the imitative tendency of social learning, while its cooperative tendency in turn relies on social learning and players' decision preferences. By analyzing a series of key experiments, we show that the above coupled dynamics can explain human behavior in game interactions. Furthermore, we find that individual learning can promote cooperation when its probability is negatively correlated with payoffs, a mechanism rooted in the psychological tendency to avoid trial-and-error when individuals are satisfied with their current payoffs. These results link long-unexplained asymmetric exploration with the cooperation-promoting ability of social networks, helping to bridge the gap between theoretical research and reality. © 2025, CC BY.

关键词： Human engineering

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Arbitrary state transition of open qubit system based on switching control

引用

Automatica 2025年 179卷

作者： Guangpu Wu Shibei Xue Shan Ma Sen Kuang Daoyi Dong Ian R. Petersen Department of Automation Shanghai Jiao Tong University Shanghai 200240 PR China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 PR China School of Automation Central South University Changsha 410083 PR China Department of Automation University of Science and Technology of China Hefei 230027 PR China Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology Sydney Ultimo 2007 NSW Australia School of Engineering Australian National University Canberra ACT 2601 Australia

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. This strategy can prevent the state from entering invariant or singularity sets, effectively driving the system ultimately to a sufficiently small neighborhood of target states, which relaxes strict constraints on system models imposed by special target states compared to existing works. For the arbitrary transition, practical finite-time stability (PFTS) and its contractive version (PFTCS) conditions are given, respectively, where asymptotic stability is unattainable in open quantum systems in general. The efficacy of our method is demonstrated in examples of a qubit affected by amplitude damping, dephasing, and depolarizing decoherence.

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Knowledge-based prediction of network controllability robustness

arXiv

引用

arXiv 2020年

作者： Lou, Yang He, Yaodong Wang, Lin Tsang, Kim Fung Chen, Guanrong The Department of Electrical Engineering City University of Hong Kong Hong Kong The Department of Automation Shanghai Jiao Tong University China The Key Laboratory of System Control and Information Processing

Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming or even infeasible. In the present paper, an improved method for predicting the network controllability robustness is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor;2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity. © 2020, CC BY.

关键词： Complex networks

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Joint Energy Trading and Scheduling for Multi-Energy microgrids with Storage

Joint Energy Trading and Scheduling for Multi-Energy microgr...

引用

第三十九届中国控制会议

作者： Dafeng Zhu Bo Yang Qi Liu Kai Ma Shanying Zhu Xinping Guan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Electrical Engineering Yanshan University

Considering that energy trading might not completely absorb excess renewable energy, we propose a multi-energy management framework including fuel cell vehicles, energy storage, combined heat and power system, and renewable energy to further realize the local absorption of renewable energy and enhance the economic benefits of microgrids. In order to reduce the influence of multi-energy coupling, stochastic renewable energy generation and energy demand, this paper designs a joint energy scheduling and trading algorithm based on Lyapunov optimization and a double-auction mechanism to optimize the long-term energy cost of each microgrid. Coupling with the auction mechanism is a dynamic, efficient energy trading and scheduling algorithm, which carefully decides the valuations of energy in the auction, optimally schedules energy, and judiciously purchases and sells energy on the current electricity prices. Simulations based on real data show that each individual microgrid, by carrying out the proposed algorithm, can achieve a time-averaged profit arbitrarily close to an optimum value while not compromising its own comfort.

关键词： Multi-energy microgrid energy storage energy trading Lyapunov optimization double-auction

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