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IEEE/cAA Journal of Automatica Sinica 2024年第3期11卷 649-660页

作者： Meilin li Yue long Tieshan li Hongjing liang c.l.philip chen the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China IEEE the Yangtze Delta Region Institute University of Electronic Science and Technology of ChinaHuzhou 313000 the laboratory of Electromagnetic Space cognition and Intelligent control Beijing 100089China the School of computer Science and Engineering South China University of TechnologyGuangzhou 510006

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first ***,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale *** is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event *** addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable ***,the results are extended to regional consensus of the MASs with the bounded control *** simulations show the effectiveness of the proposed approach.

关键词： constrained input designable minimum inter-event time directed communication topology dynamic event-triggered mechanism MASs consensus control

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Adaptive neural network control of a 2-DOF helicopter system considering input constraints and global prescribed performance

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Science china(Information Sciences) 2024年第7期67卷 224-239页

作者： Zhijia ZHAO Jiale WU Zhijie lIU We HE c.l.philip chen School of Mechanical and Electrical Engineering Guangzhou University School of Intelligence Science and Technology and Key laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education University of Science and Technology Beijing School of computer Science and Engineering South China University of Technology Pazhou lab

In this study, an adaptive neural network(NN) control is proposed for nonlinear two-degree-offreedom(2-DOF) helicopter systems considering the input constraints and global prescribed ***, radial basis function NN(RBFNN) is employed to estimate the unknown dynamics of the helicopter system. Second, a smooth nonaffine function is exploited to approximate and address nonlinear constraint functions. Subsequently, a new prescribed function is proposed, and an original constrained error is transformed into an equivalent unconstrained error using the error transformation and barrier function transformation methods. The analysis of the established lyapunov function proves that the controlled system is globally uniformly bounded. Finally, the simulation and experimental results on a constructed Quanser's test platform verify the rationality and feasibility of the proposed control.

关键词： adaptive NN control 2-DOF helicopter global prescribed performance input constraints

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Broad learning system based on the quantized minimum error entropy criterion

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Science china(Information Sciences) 2022年第12期65卷 204-219页

作者： Simin ZHANG Zhulin lIU c.l.philip chen School of computer Science and Engineering South China University of Technology Pazhou lab

The broad learning system(BlS) based on the minimum mean square error(MMSE) criterion can achieve outstanding performance without spending too much time in various machine learning ***, when data are polluted by non-Gaussian noise, the stability of BlS may be destroyed because the MMSE criterion is sensitive to outliers. Different from the MMSE criterion, the minimum error entropy(MEE) criterion utilizes the kernel function to capture high-dimensional information and decrease the negative influence of outliers, which will make BlS more discriminative and robust. Nevertheless, the computational complexity of MEE is high due to a double summation of the data size. To solve these issues, this paper proposes a new robust BlS variant based on the quantized minimum error entropy(QMEE) criterion, in which a quantization operation is used to reduce the computational complexity of MEE. The proposed model BlS-QMEE is optimized by the fixed-point iterative method, and a sufficient condition for its convergence is provided. compared with the standard BlS and other existing robust variants of BlS, BlS-QMEE performs more satisfactorily without consuming too much time. The desirable performance of BlS-QMEE is verified by various experiments on function approximation, several public datasets, and a practical application.

关键词： broad learning system quantized minimum error entropy robustness minimum error entropy convergence

Target tracking algorithm based on a broad learning system

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Science china(Information Sciences) 2022年第5期65卷 261-263页

作者： Dan ZHANG Tieshan lI c.l.philip chen He YANG Navigation college Dalian Maritime University Innovation and Entrepreneurship Education college Dalian Minzu University School of Automation Engineering University of Electronic Science and Technology of China computer Science and Engineering college South China University of Technology

Target tracking is one of the most important research topics in the field of computer vision. It has been widely applied in aerospace, automatic monitoring, navigation, human-computer interaction, and artificial intel... 详细信息

关键词： Target tracking Broad learning system Selective search algorithm Multi-cue tracking Tracking adaptability

Target tracking method of Siamese networks based on the broad learning system

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cAAI Transactions on Intelligence Technology 2023年第3期8卷 1043-1057页

作者： Dan Zhang c.l.philip chen Tieshan li Yi Zuo Nguyen Quang Duy Navigation college Dalian Maritime UniversityDalianChina Innovation and Entrepreneurship Education college Dalian Minzu UniversityDalianChina computer Science and Engineering college South China University of TechnologyGuangzhouChina Department of computer and Information Science Faculty of Science and TechnologyUniversity of MacaoMacaoChina School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina Faculty of Navigation Vietnam Maritime UniversityHaiphongVietnam

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other ***,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time *** based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time ***,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during ***,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time *** broad learning system(BlS)has a simple network structure,high learning efficiency,and strong feature learning *** at the problems of Siamese networks and the characteristics of BlS,a target tracking method based on BlS is *** method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BlS for re‐learning and re‐*** broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the *** results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

关键词： broad learning system siamese network target tracking

Dynamic Event-Triggered consensus control for Input constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

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Performance analysis of fuzzy BlS using different cluster methods for classification

自动化学报（英文版） 2024年第3期

作者： Meilin li Yue long Tieshan li Hongjing liang c.l.philip chen

来源：博看期刊评论

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Science china(Information Sciences) 2021年第4期64卷 232-234页

作者： Shuang FENG c.l.philip chen School of Applied Mathematics Beijing Normal University Faculty of Science and Technology University of Macau

Dear editor,Neural networks (NNs) and fuzzy systems are commonly used computational intelligence techniques, each with their own merits in terms of applications. The integration of NNs and fuzzy systems, which leads to a hybrid framework known as neuro-fuzzy systems, inherits the useful properties of its constituents:the learning power of an NN and the knowledge representation of a fuzzy inference system,

关键词： fuzzy broad learning system clustering fuzzy c-means classification TSK fuzzy system

Discriminative graph regularized broad learning system for image recognition

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Science china(Information Sciences) 2018年第11期61卷 179-192页

作者： Junwei JIN Zhulin lIU c.l.philip chen Faculty of Science and Technology University of Macau Dalian Maritime University State Key laboratory of Management and control for complex Systems Institute of AutomationChinese Academy of Sciences

Broad learning system(BlS) has been proposed as an alternative method of deep learning. The architecture of BlS is that the input is randomly mapped into series of feature spaces which form the feature nodes, and the output of the feature nodes are expanded broadly to form the enhancement nodes, and then the output weights of the network can be determined analytically. The most advantage of BlS is that it can be learned incrementally without a retraining process when there comes new input data or neural nodes. It has been proven that BlS can overcome the inadequacies caused by training a large number of parameters in gradient-based deep learning algorithms. In this paper, a novel variant graph regularized broad learning system(GBlS) is proposed. Taking account of the locally invariant property of data, which means the similar images may share similar properties, the manifold learning is incorporated into the objective function of the standard BlS. In GBlS, the output weights are constrained to learn more discriminative information,and the classification ability can be further enhanced. Several experiments are carried out to verify that our proposed GBlS model can outperform the standard BlS. What is more, the GBlS also performs better compared with other state-of-the-art image recognition methods in several image databases.

关键词： broad learning system deep learning graph regularization image recognition feature extraction incremental learning

A Survey of Human-centered Intelligent Robots:Issues and challenges

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IEEE/cAA Journal of Automatica Sinica 2017年第4期4卷 602-609页

作者： Wei He Zhijun li c.l.philip chen IEEE School of Automation and Electrical Engineering University of Science and Technology Beijing college of Automation Science and Engineering South China University of Technology Department of computer and Information Science Faculty of Science and Technology University of Macao

Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.

关键词： Human-centered robots human-robot interaction intelligent control navigation path planning pattern recognition

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Integral Barrier lyapunov function-based adaptive control for switched nonlinear systems

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Science china(Information Sciences) 2020年第3期63卷 212-225页

作者： lei lIU Yan-Jun lIU Aiqing chen Shaocheng TONG c.l.philip chen college of Science Liaoning University of Technology computer Science and Engineering South China University of Technology Navigation college Dalian Maritime University

This paper presents an adaptive control method for a class of uncertain strict-feedback switched nonlinear systems. First, we consider the constraint characteristics in the switched nonlinear systems to ensure that all states in switched systems do not violate the constraint ranges. Second, we design the controller based on the backstepping technique, while integral Barrier lyapunov functions(i BlFs) are adopted to solve the full state constraint problems in each step in order to realize the direct constraints on state ***, we introduce the lyapunov stability theory to demonstrate that the adaptive controller achieves the desired control goals. Finally, we perform a numerical simulation, which further verifies the significance and feasibility of the presented control scheme.

关键词： adaptive control switched nonlinear systems integral Barrier lyapunov functions backstepping technique full state constraints