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Adaptive output feedback control for uncertain nonholonomic chained systems

Journal of Central South University 2010年第3期17卷 572-579页

作者：袁占平王祝萍陈启军 Department of Electronics and Information Engineering Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

An adaptive output feedback control was proposed to deal with a class of nonholonomic systems in chained form with strong nonlinear disturbances and drift terms. The objective was to design adaptive nonlinear output feedback laws such that the closed-loop systems were globally asymptotically stable, while the estimated parameters remained bounded. The proposed systematic strategy combined input-state-scaling with backstepping technique. The adaptive output feedback controller was designed for a general case of uncertain chained system. Furthermore, one special case was considered. Simulation results demonstrate the effectiveness of the proposed controllers.

关键词： adaptive output feedback input-state scaling backstepping nonholonomic system

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Prescribed-time synchronization for multiweighted complex networks: A switching control scheme

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systems & Control Letters 2025年 203卷

作者： Linlong Xu Xiwei Liu Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 201804 PR China

This letter deals with an interesting intersection phenomenon of prescribed-time stability (PTSta) for dynamical systems, and develops a novel switching control scheme to investigate prescribed-time synchronization (PTSyn) for multiweighted and directly coupled complex networks. Different from most previous works that scholars only pay attention to designing the regulation function to ensure PTSta, we aim to select various parameters and uncover the mathematical mechanism of intersecting system state curves. We rigorously prove that, if the settling time is larger than 1, then no matter what the initial value is, the intersection exists only once before the settling time; otherwise, there is no intersection. Moreover, an energy consumption evaluation function is also put forward for PTSta, whose exact value is also calculated via exponential integrals. Then, this intersection theory is applied on the PTSyn of multiweighted complex networks, which is beneficial to construct the switching control scheme or choose the optimal parameters to reduce the energy cost. The rearranging variables’ order technique is utilized to conduct the multiweighted complex networks and obtain the synchronization criterion. Finally, four simulations are presented to verify theoretical results.

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Bayesian cognitive trust model based self-clustering algorithm for MANETs

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Science China(Information Sciences) 2010年第3期53卷 494-505页

作者： WANG Wei1,2,3 & ZENG GuoSun1,2,31Department of Computer Science & Technology, Tongji University, Shanghai 200092, China 2Tongji Branch, National Engineering & Technology Center of High Performance Computer, Shanghai 200092, China 3key loboratory of embedded system and service computing, Ministry of Education, Shanghai 200092, China Department of Computer Science & Technology Tongji University Shanghai China Tongji Branch National Engineering & Technology Center of High Performance Computer Shanghai China Key Loboratory of Embedded System and Service Computing Ministry of Education Shanghai China

With the introduction of mobile Ad hoc networks (MANETs), nodes are able to participate in a dynamic network which lacks an underlying infrastructure. Before two nodes agree to interact, they must trust that each will satisfy the security and privacy requirements of the other. In this paper, using the cognition inspired method from the brain informatics (BI), we present a novel approach to improving the search efficiency and scalability of MANETs by clustering nodes based on cognitive trust mechanism. The trust relationship is formed by evaluating the level of trust using Bayesian statistic analysis, and clusters can be formed and maintained autonomously by nodes with only partial knowledge. Simulation experiments show that each node can form and join proper clusters, which improve the interaction performance of the entire network. The essence of the underlying reason is analyzed through the theory of complex networks, revealing great scalability of this method.

关键词： trust model MANET self-clustering Bayesian method cognitive mobile

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Time-varying networks based on activation and deactivation mechanisms

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Chinese Physics B 2017年第10期26卷 527-532页

作者：王学文罗月娥张丽杰许新建 School of Physics and Electronic Sciences Guizhou Education University College of Sciences Shanghai University Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education

A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions. The evolution of the network couples the addition of new nodes and state transitions of old ones. The active group changes with activated nodes entering and deactivated ones leaving. A general differential equation framework is developed to study the degree distribution of nodes of integrated networks where four different schemes are formulated.

关键词： time-varying networks activity-driven degree distribution

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Geometric Algebra Multi-order Graph Neural Network for Traffic Prediction

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IEEE Transactions on Big Data 2024年第3期11卷 1-15页

作者： Zang, Di Cui, Zhe Wang, Zengqiang Lei, Juntao Ding, Yongjie Wei, Chenguang Zhang, Junqi Department of Computer Science and Technology and the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic prediction. However, traditional graph neural networks only capture pairwise spatial relationships between road network nodes, neglecting high-order interactions among multiple nodes. Meanwhile, most work for extracting temporal dependencies suffers from implicit modeling and overlooks the internal and external dependencies of time series. To address these challenges, we propose a Geometric Algebraic Multi-order Graph Neural Network (GA-MGNN). Specifically, in the temporal dimension, we design a convolution kernel based on the rotation matrix of geometric algebra, which not only learns internal dependencies between different time steps in time series but also external dependencies between time series and convolution kernels. In the spatial dimension, we construct a tokenized hypergraph and integrate dynamic graph convolution with attention hypergraph convolution to comprehensively capture multi-order spatial dependencies. Additionally, we design a segmented loss function based on traffic periodic information to further improve prediction accuracy. Extensive experiments on seven real-world datasets demonstrate that GAMGNN outperforms state-of-the-art baselines IEEE

关键词： Graph neural networks

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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A QUANTUM MULTI-AGENT BASED NEURAL NETWORK MODEL FOR FAILURE PREDICTION

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Journal of systems Science and systems Engineering 2016年第2期25卷 210-228页

作者： Wei Wu Min Liu Qing Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai 200092 China

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the Laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.

关键词： Failure prediction complex equipment quantum-inspired multi-agent algorithm back propagation neural network

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A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

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The influence of limited visual sensing on the Reynolds flocking algorithm 3

The influence of limited visual sensing on the Reynolds floc...

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3rd IEEE International Conference on Robotic computing (IRC)

作者： Soria, Enrica Schiano, Fabrizio Floreano, Dario Department of Computer Science and Technology the Key Laboratory of Embedded System Service Computing Shanghai China

ISBN: (纸本)9781538692455

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which consists in the analysis of the effects of limited sensing on the behavior of the swarm. In nature, the central sensor modality often used for achieving flocking is vision. In this work, we study how the reduction in the field of view and the orientation of the visual sensors affect the performance of the Reynolds flocking algorithm used to control the swarm. To quantify the impact of limited visual sensing, we introduce different metrics such as (i) order, (ii) safety, (iii) union and (iv) connectivity. As Nature suggests, our results confirm that lateral vision is essential for coordinating the movements of the individuals. Moreover, the analysis we provide will simplify the tuning of the Reynolds flocking algorithm which is crucial for real-world deployment and, especially for aerial swarms, it depends on the envisioned application. We achieve the results presented in this paper through extensive Monte-Carlo simulations and integrate them with the use of genetic algorithm optimization.

关键词： Robot sensing systems Visualization Animals Nickel Azimuth

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