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Broad-Learning-System-Based Model-Free Adaptive Predictive control for Nonlinear MASs Under DoS Attacks

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 381-393页

作者： Hongxing Xiong Guangdeng Chen Hongru Ren Hongyi Li the School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology the College of Electronic and Information Engineering Southwest University IEEE

In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.

关键词： Broad learning technique denial-of-service (DoS) model-free adaptive predictive control (MFAPC) nonlinear multi-agent systems (NMASs)

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Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

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Journal of Systems Engineering and Electronics 2024年第2期35卷 361-373页

作者： CHEN Chen QUAN Wei SHAO Zhuang School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced ***, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

关键词： target threat assessment gated recurrent unit(GRU) self-attention(SA) fractional Fourier transform(FRFT)

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Neural-based prescribed-time consensus control for multiagent systems via dynamic memory event-triggered mechanism

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Science China(Technological Sciences) 2025年第3期68卷 217-227页

作者： Xiaohong ZHENG Hui MA Qi ZHOU Hongyi LI School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Controland Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative ControlGuangdong University of TechnologyGuangzhou510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou510006China College of Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing400715China

This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN *** method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity ***,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control *** notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control ***,the DMETM is established to reduce the actuation frequency of the *** the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering *** a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed ***,simulation results are presented to validate the effectiveness of the proposed algorithm.

关键词： consensus control composite learning control dynamic memory event-triggered control prescribed-time control multiagent systems(MASs)

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Partition and planning:A human-like motion decision for UAV in trap environment

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Science China(Technological Sciences) 2024年第4期67卷 1226-1237页

作者： CHEN JinTao ZHOU Qi REN HongRu LI HongYi School of Automation Guangdong University of TechnologyGuangzhou 510006China Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou 510006Chi

This paper presents a human-like motion decision-making method for unmanned aerial vehicles(UAVs)navigating in trap *** proposed a space partitioning method based on sampling and consistency control to conduct a preliminary analysis of the indoor environment based on architectural *** method reduces the dimensionality of the path planning problem,thereby enhancing the ***,we designed a target-switching logic for the dynamic window *** improvement endows the UAV with the capability of both real-time obstacle avoidance and global navigation,enhancing the efficiency of the UAV in flying to task spots ***,by applying human-like methods of batch distance perception and obstacle perception to this scheme,we have further enhanced the robustness and efficiency of path ***,considering the scenario of high-rise fire rescue,we conducted simulation *** demonstrates that our scheme enhances the efficiency and robustness of path planning.

关键词： path planning indoor environment spacial partition high-rise fire rescue

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Practically fixed-time adaptive consensus control for multiagent systems with prescribed performance

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Science China(Technological Sciences) 2024年第12期67卷 3867-3876页

作者： ZHENG ShuaiPeng MA Hui REN HongRu LI HongYi School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Controland Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative ControlGuangdong University of TechnologyGuangzhou 510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou 510006China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China

In this paper,the fixed-time consensus tracking control problem of multiagent systems(MASs)subject to unknown nonlinearities and performance constraints is ***,an improved fixed-time performance function is designed,which enables the consensus tracking errors to converge to the preset region in fixed time,and alleviates the initial error conditions by setting the parameters ***,the unknown nonlinearities of MASs are approximated by the radial basis function neural network(RBF NN).Subsequently,a fixed-time prescribed performance controller is designed,which excludes the fractional power of tracking error to prevent potential singularity problems existing in stability ***,a fixed-time dynamic surface filter is formulated to eliminate the“explosion of complexity”issue,meanwhile,the filter errors are bounded in fixed *** the Lyapunov stability theory,it can be guaranteed that all signals in MASs exhibit practically fixed-time stability,and the consensus errors all approach a small region centered on origin within the prescribed ***,simulations are presented to verify the validity of the proposed control strategy.

关键词： dynamic surface filter fixed-time control multiagent systems(MASs) prescribed performance

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A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot

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无人系统（英文） 2023年第2期11卷 159-173页

作者： Liling Ma Jian Guo Jiehao Li Junzheng Wang State Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of TechnologyBeijing 100081P.R.China

This research provides a novel approach for detecting multi-legged robot actuator *** most significant concept is to design the Fault Diagnosis Generative Adversarial Network(FD-GAN)to fully adapt to the fault diagnosis problem with insufficient *** found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data.A straightforward solution is to use massive amounts of normal data to drive the diagnostic *** introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data.A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal *** uses a generator network as a feature extractor,and uses a discriminator network as a fault probability evaluator,which creates a new use of GAN in the field of fault *** the many learning strategies of GAN,we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the *** also design a fault location method based on binary search,which greatly improves the search efficiency and engineering value of the entire *** have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working *** compared FD-GAN with popular diagnostic *** results show that our method has the highest accuracy and recall rate.

关键词： Actuator fault diagnosis multi-legged robot generative adversarial network attention mechanism

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A highly-selective biomimetic potassium channel

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National Science Review 2024年第8期11卷 187-195页

作者： Junliang Zhu Hu Qiu Wanlin Guo Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education State Key Laboratory of Mechanics and Control for Aerospace Structures Institute for Frontier ScienceNanjing University of Aeronautics and Astronautics

Reproducing the outstanding selectivity achieved by biological ion channels in artificial channel systems can revolutionize applications ranging from membrane filtration to single-molecule sensing technologies,but achieving this goal remains a ***,inspired by the selectivity filter structure of the KcsA potassium channel,we propose a design of biomimetic potassium nanochannels by functionalizing the wall of carbon nano tubes with an array of arranged carbonyl oxygen *** extensive molecular dynamics simulations show that the biomimetic nanochannel exhibits a high K+permeation rate along with a high K+/Na+selectivity *** free energy calculations suggest that the low Na+permeability is the result of the higher energy barrier for Na+than K+at the channel entrance and ion binding *** addition,reducing the number of ion binding sites leads to an increase in the permeation rate but a decrease in *** findings not only hold promise for the design of high-performance membranes but also help understand the mechanism of selective ion transport in biological ion channels.

关键词： biomimetic design carbon nanotube K+/Na+ selectivity molecular dynamics simulations

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Deep reinforcement learning for optimal denial-of-service attacks scheduling

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Science China(Information Sciences) 2022年第6期65卷 98-106页

作者： Fangyuan HOU Jian SUN Qiuling YANG Zhonghua PANG State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology

We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.

关键词： optimal denial-of-service attack scheduling optimization limited energy deep reinforcement learning

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Distributed multiple model filtering for markov jump systems with communication delays

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Journal of the Franklin Institute 2023年第4期360卷 3407-3435页

作者： Li, Hui Yan, Liping Xia, Yuanqing Zhang, Jinhui the Key Laboratory of Intelligent Control Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

In this paper, we study a distributed state estimation problem for Markov jump systems (MJS) over sensor networks, in which each sensor node connects with each other through wireless networks with communication delays. We assume that each sensor node maintains a buffer to store delayed data transmitted from neighbor nodes. A distributed multiple model filter is designed by using the interacting multiple model methods (IMM) and a recursive delays compensation method. In order to ensure the stability, two stability conditions are derived for boundedness of estimation errors and boundedness of error covariance. Finally, the effectiveness of the proposed methods is illustrated by simulations and experiments of maneuvering target tracking. © 2022 The Franklin Institute

关键词： Sensor nodes

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Leader-following consensus control for multi-agent systems under DoS attacks and average dwell time switching topology

Leader-following consensus control for multi-agent systems u...

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2024 China Automation Congress, CAC 2024

作者： Yuan, Li Liu, Shuai Li, Panshuo Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou Guangzhou510006 China

ISBN: (纸本)9798350368604

This article studies the leader-following consensus problem of multi-agent systems (MASs) in the presence of denial-of-service (DoS) attacks and switching topology. The problem that all edges of one follower are blocked under each attack is considered in this work. By using the multiple Lyapunov functions approach and average dwell time switching technique, a piecewise consensus protocol is designed. Sufficient conditions are established for the consensus of linear MAS. Moreover, the lower bound of average dwell time of the switching topology is determined under DoS attacks. Finally, we provided simulations to validate our theoretical finding. © 2024 IEEE.

关键词： Multi agent systems

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