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American control Conference (ACC)

作者： Ruchao Su Xianwei Li Shaoyuan Li Department of Automation Shanghai Jiao Tong University Shanghai China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We investigate the consensus problem of multi-agent systems with linear dynamics via a fully distributed event-triggered approach. To solve this problem, we present a fully distributed control strategy with an innovative event-triggered mechanism, where a clock variable is introduced to guarantee the existence of strictly positive minimum inter-event times (MIETs). Compared with existing results, the proposed event-triggered mechanism provides more flexibility for the clock variable, improving inter-event times. A positive lower bound of MIETs is derived, and is shown to be larger than those in previous works. The main results are illustrated with a numerical example.

关键词： Decentralized control Directed graphs Clocks Multi-agent systems

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Energy-Efficient Safety-Aware Scheduling of Real-Time control Systems with Burst Tasks

Energy-Efficient Safety-Aware Scheduling of Real-Time Contro...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Ting Cheng Yonghui Liang Qimin Xu Shanying Zhu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In industrial sites, multiple real-time control systems often share computing resources. However, burst computing tasks may lead to the random dropping of control computing subtasks, resulting in control failures and potential hazards. To address this issue, we propose an energy-efficient safety-aware task scheduling scheme based on mixed-integer programming. When burst computing tasks are triggered, this scheduling scheme adaptively releases resources from low-criticality control computing sub tasks and adjusts the processor speed based on the dynamic voltage and frequency scaling technology (DVFS), ensuring the timely completion of burst computing tasks and keeping the control system state deviation within a safe threshold. To achieve the scheduling scheme, we propose an efficient algorithm called EESASA. Simulation results show that EESASA can minimize the overall cost of control and processor energy while ensuring the safety of the control system and the timely completion of burst computing tasks.

关键词： Time-frequency analysis Job shop scheduling Costs Processor scheduling Process control control systems Dynamic scheduling Real-time systems Energy efficiency Voltage control

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H∞ Consensus control for High-Order Fully Actuated Multi-Agent Systems with External Disturbances

H∞ Consensus Control for High-Order Fully Actuated Multi-Ag...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Qing-Hao Zhang Jun-Guo Lu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper investigates the problems of H ∞ consensus control for high-order fully actuated multi-agent systems (HOFAMASs) with external disturbances. By utilizing the characteristics of high-order fully actuated systems (HOFASs), the distributed control protocols are designed, eliminating all nonlinear characteristics of open loop systems while establishing new closed-loop dynamic characteristics. Then, the H ∞ consensus problems of HOFAMASs are addressed. The corresponding parameters to be designed in the distributed control protocols are given by using linear matrix inequalities (LMIs). A novel multi-spacecraft attitude model is proposed based on HOFASs. An illustrative example of the multi-spacecraft attitude model is given, in order to demonstrate the effectiveness and application of the results proposed in this paper.

关键词： Sufficient conditions Protocols Attitude control Open loop systems Consensus control Numerical models Linear matrix inequalities

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Double-Swarm Improved Moth-Flame Optimization Algorithm with Golden Sine for Global Optimization

Double-Swarm Improved Moth-Flame Optimization Algorithm with...

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Chinese control Conference (CCC)

作者： Xiaodong Zhao Zhuang Ma Zhiqiang Hu School of Mathematics and Statistics Taishan University Taian China Key Laboratory of Intelligent Data Processing and Control of Hebei Province Tangshan University Tangshan China

Moth-flame optimization algorithm (MFO) is a swarm intelligent optimization algorithm, which is proposed by simulating the night navigation mechanism of moths. MFO is widely used in various fields since it has a simple structure and easy implementation. However, in the process of MFO search, the population diversity will deteriorate, and premature convergence will occur. To solve the above problems, a double-swarm improved moth-flame optimization algorithm with golden sine operator(DIMFO) is proposed in this paper. The population is grouped by chaos mechanism, and the information exchange between sub-swarms is improved by dynamic recombination. The spiral search and golden sine search are used for the two moth populations respectively, and the Gaussian mutation is utilized to generate flames, which improves the population diversity. In addition, compared with other improved MFO algorithm and advanced optimization algorithms in the classical test set, the results show that DIMFO has advantages in convergence accuracy and speed in most test problems.

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Autonomous Path Planning via Sand Cat Swarm Optimization With Multi-Strategy Mechanism for Unmanned Aerial Vehicles in Dynamic Environment

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IEEE Internet of Things Journal 2025年

作者： Deng, Wu Feng, Jiayi Zhao, Huimin Civil Aviation University of China College of Electronic Information and Automation Tianjin300300 China Civil Aviation University of China Tianjin Key Lab Intelligent Signal and Image Processing Tianjin300300 China State Key Laboratory of Rail Transit Vehicle System of Southwest Jiaotong University Chengdu610031 China

Unmanned Aerial Vehicles (UAVs) face critical challenges in path planning in dynamic environment. Sand Cat Swarm Optimization (SCSO) algorithm has demonstrated promise in addressing complex optimization challenges. However, SCSO is limited by slow convergence, susceptibility to local optima, and insufficient adaptability. To overcome these shortcomings, an enhanced SCSO with the spiral search, Livy flight, tent chaotic mapping, and adaptive sparrow alert mechanism, namely TSLS-SCSO is developed to propose an autonomous path planning method for UAVs in dynamic environment. In TSLS-SCSO, a new population initialization strategy with tent chaotic mapping is designed to achieve a large dynamic range and coverage capability. Then a new spiral search strategy is designed to broaden the search range in the search phase. And a new Livy flight strategy is employed to enrich the diversity of population in the attacking prey phase. A new sparrow alert mechanism with integrating the sand cat group with the sparrow alert is designed to obtain faster convergence speed and accuracy. The experiment results on CEC 2017 and CEC 2022 show that the TSLS-SCSO obtains higher accuracy and more stable solutions and exhibits better competitiveness. Furthermore, the proposed UAV path planning method successfully found effective paths with an obstacle avoidance effectiveness of 95.5%. The obtained results validate the effectiveness and competitiveness of TSLS-SCSO in UAV path planning in the dynamic environment. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Semantic Fusion and Contrastive Generation for Generalized Zero-Shot Learning

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International Journal of Multimedia Information Retrieval 2025年第3期14卷 1-11页

作者： Yang, Guan Sun, Weihao Liu, Xiaoming Liu, Yang Wang, Chen School of Artificial Intelligence Zhongyuan University of Technology Zhengzhou China School of Computer Science Zhongyuan University of Technology Zhengzhou China Zhengzhou Key Laboratory on Text Processing and Image Understanding Zhongyuan University of Technology Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhongyuan University of Technology Zhengzhou China School of Telecommunications Engineering Xidian University Xi’an China

Generalized Zero-Shot Learning (GZSL) aims to leverage a classifier trained on seen classes to categorize instances from both seen and unseen classes. Several approaches have been introduced to synthesize visual features that simulate those of unseen classes for training classifiers. However, existing methods only emphasize the distributional relationships between synthesized and real features, while neglecting the inter-class relationships among the synthesized features. Consequently, synthesized visual features exhibit significant loose intra-class distributions and numerous outliers. Furthermore, the generator trained solely on seen classes tend to overfit these classes. In this paper, a Semantic Fusion and Contrastive Generation (SFCG) framework is proposed for GZSL. Specifically, a visual-semantic contrastive generation method and a visual features similarity loss are explored to address the challenges of loose intra-class distribution and outliers in synthesized visual features. Moreover, semantic attributes are fused to create novel and diverse semantic instances for training a balanced generator. The SFCG model is evaluated on four widely-used ZSL benchmark datasets: CUB, FLO, AWA2, and SUN. It achieves harmonic mean accuracies of 68.4% on CUB, 71.3% on FLO, 73.4% on AWA2, and 45.2% on SUN, demonstrating the efficacy of the proposed method.

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Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols

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Science China(Information Sciences) 2022年第1期65卷 56-71页

作者： Yajing ZHANG Qimin XU Xinping GUAN Cailian CHEN Mingyan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management College of Computer Science Chongqing University

The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems(ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service(Qo S) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5 th generation(5 G) wireless communication technology provides ultra-reliable and low-latency communication(URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking(TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network(HTSN) co-designed by 5 G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling(SPS) to reduce signaling delay by reserving resources in *** we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing(PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization.

关键词： industrial cyber-physical systems (ICPS) 5G-TSN co-design wireless/wired integrated network risk-sensitive reinforcement learning deterministic communication

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Research on Optimization of Two-Stage Scheduling for Aluminum Extrusion Machine Manufacturing Line

Research on Optimization of Two-Stage Scheduling for Aluminu...

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Chinese Automation Congress (CAC)

作者： Aoran Zhou Lipeng Qiu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In view of the problems of high energy consumption, long inventory time and low production efficiency in the production process of traditional aluminum extrusion machine, this paper establishes an efficient two-stage parallel scheduling optimization model based on the integration of flexible job shop scheduling and assembly sequence planning (IFJSS-ASP). On this basis, an evolutionary algorithm combining strong dominance relationship and simple grouping method (SDREA-SGM) is proposed. The algorithm utilizes a two-stage hybrid chromosome encoding and decoding method to establish a rational mapping from the decision space to the solution space. Furthermore, in order to prevent the model from easily falling into local optima, an adaptive intra-class and inter-class pairing selection strategy has been adopted by the algorithm. This enhances global search and improves local search flexibility. Finally, the feasibility and effectiveness of the algorithm are verified by solving problems of different scales.

关键词： Energy consumption Job shop scheduling Optimization models Aluminum Production Evolutionary computation Encoding Planning Decoding Optimization

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Modeling of All-Vanadium Redox Battery Based on Dual Stream Network with Attention Mechanism

Modeling of All-Vanadium Redox Battery Based on Dual Stream ...

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International Conference on Computer and Communications (ICCC)

作者： Jiajie Zhu Bo Yang Mingxuan Cai Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Accurate voltage prediction for all-vanadium redox batteries (VRBs) affects the efficiency and safety of battery. In this paper, we propose a self-attention-based dual-stream neural network (SA-DSNN) model for voltage prediction under various working conditions. This approach independently processes temporal and condition data streams to reduce the noise interference. Using neural networks tailored to each of stream can more effectively extract intrinsic features. In particular, self-attention mechanisms are employed to enhance critical information. The features from both streams are fully integrated in output layer to generate final predictions. Experimental results indicate that the proposed method enhances the generalization and accuracy of VRBs model across different time scale.

关键词： Accuracy Neural networks Noise Voltage Interference Predictive models Redox Feature extraction Batteries Streams

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Distributed Optimization of Heterogeneous Linear Multi-Agent Systems with Unknown Disturbances

Distributed Optimization of Heterogeneous Linear Multi-Agent...

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IEEE Conference on Decision and control

作者： Mengmeng Duan Shanying Zhu Ziwen Yang Cailian Chen Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. By applying the primal-dual method and the time-scale separation technique, a distributed dynamic controller without using any disturbance information is proposed. Based on the optimal condition, the relationship between the optimal solution and the equilibrium point of the system is established, and it is shown that the distributed optimization problem is solved provided that the closed-loop system is stable. Then, inspired by the nonsingular perturbation analysis, it is proved that the closed-loop system is exponentially input-to-state stable with respect to the derivative of the disturbance. Numerical examples are provided to illustrate the theoretical results.

关键词： Perturbation methods Trajectory Closed loop systems Optimization Multi-agent systems

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