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arXiv 2025年

作者： Cui, Bohan Yu, Xinyi Giua, Alessandro Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Thomas Lord Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Electrical and Electronic Engineering University of Cagliari Cagliari09123 Italy

In this paper, we investigate the control synthesis problem for Signal Temporal Logic (STL) specifications in the presence of uncontrollable agents. Existing works mainly address this problem in a robust control setting by assuming the uncontrollable agents are adversarial and accounting for the worst-case scenario. While this approach ensures safety, it can be overly conservative in scenarios where uncontrollable agents have their own objectives that are not entirely opposed to the system’s goals. Motivated by this limitation, we propose a new framework for STL control synthesis within the Stackelberg game setting. Specifically, we assume that the system controller, acting as the leader, first commits to a plan, after which the uncontrollable agents, acting as followers, take a best response based on the committed plan and their own objectives. Our goal is to synthesize a control sequence for the leader such that, for any rational followers producing a best response, the leader’s STL task is guaranteed to be satisfied. We present an effective solution to this problem by transforming it into a single-stage optimization problem and leveraging counterexample guided synthesis techniques. We demonstrate that the proposed approach is sound and identify conditions under which it is optimal. Simulation results are also provided to illustrate the effectiveness of the proposed framework. © 2025, CC BY.

关键词： control system synthesis

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Vector Angular Spectrum Model for light travelling in scattering media

arXiv

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arXiv 2024年

作者： Liu, Kaige Zhang, Hengkang Liu, Zeqi Zhang, Bin Fu, Xing Yuan, Qiang Liu, Qiang Ministry of Education Beijing100084 China State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Beijing100084 China Research Center of Laser Fusion China Academy of Engineering Physics Mianyang621900 China Beijing Institute of Control Engineering Beijing100190 China Beijing Institute of Electronic System Engineering Beijing100854 China

Strongly scattering media disrupt both the wavefront distribution and the polarization state of the incident light field. controlling and effectively utilizing depolarization effects are crucial for optical applications in highly scattering environments, such as imaging through dense fog. However, current simulation models have difficulty simulating the evolution of vector light fields within scattering media, posing challenges for studying vector light fields in strongly scattering environments. Here, we propose the Vector Angular Spectrum (VAS) model for simulating the propagation of vector light fields within scattering media. By introducing the angular spectrum distribution of vector light scattering and polarization conversion mechanisms, this model can simulate the depolarization effects of vector light propagating through strongly scattering media. The VAS model has also been used to investigate the focusing of vector scattered light through scattering media. Furthermore, the simulation results of the model have been validated through experiments. The proposed VAS model is expected to play a role in the theoretical research of vector scattered light and optical applications in strongly scattering environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

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Experimental Study on the Operational Characteristics and Coupling Relationships of Burners, Heat Exchangers, and Reformers in a Kerosene Solid Oxide Fuel Cell

SSRN

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SSRN 2024年

作者： Liu, Yechang Ning, Zhi Wang, Lintao Zheng, Xuan Sun, Chunhua Wei, Yongqi Lv, Ming School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Shanghai Marine Diesel Engine Research Institute Shanghai201108 China Electric Drive System Department of BAIC Research Institute Co. Ltd Beijing China

The burner, heat exchanger, and reformer are pivotal components of the solid oxide fuel cell (SOFC) power generation system, and their operational characteristics have a direct impact on the system's ability to achieve efficient and stable power output. This study has established an experimental platform for a kerosene SOFC system to investigate the operational characteristics and coupling relationships of the burner, heat exchanger, and reformer. The research findings indicate that the burner operates stably with an excess air ratio of 39.4, but experiences frequent extinguishing when the methane flow rate falls below 4 SLM. Reducing both methane and water flow rates leads to a more significant temperature decrease in the burner and combined heat exchanger compared to reducing methane flow alone. Increasing the kerosene flow rate by 15% causes a 2.5% change in the outlet temperature of the burner, a 2.15% change in the air heat exchanger's exhaust gas input temperature, and a 1.47% change in the flue gas inlet and outlet pressure drop. However, due to the presence of the heater, there is no significant variation in the input and exit temperatures of the reformer. By elevating the reformer temperature and steam-to-carbon ratio, it is possible to bring the hydrogen volume fraction after kerosene reforming closer to that after methane reforming, and even lower than that after methane reforming. The electric heater serves as a means of thermal compensation, mitigating the intricate coupling effects among the reformer, burner, and heat exchanger. © 2024, The Authors. All rights reserved.

关键词： Methane

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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

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system Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Jiahui Huang Xiangxiang Zheng Guodong Lv Xiaojing Li Guohua Wu Liang Xu Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia The First Affiliated Hospital of Xinjiang Medical University Urumqi China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached $95.7 \%$. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and $1372 \mathrm{~cm}^{-1}$ might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application.

关键词： Support vector machines Analytical models Machine learning algorithms Accuracy Biological system modeling Surface resistance Raman scattering Interference Machine learning Kernel

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Convergence Analysis and Latency Minimization for Retransmission-Based Semi-Federated Learning

Convergence Analysis and Latency Minimization for Retransmis...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingheng Zheng Wanli Ni Hui Tian Wenchao Jiang Tony Q. S. Quek State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Beijing China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore

In this paper, we propose a semi-federated learning (SemiFL) framework to ameliorate the performance of conventional federated learning. The base station and devices are coordinated to collaboratively train a shared model. However, due to the rapidly fluctuating channels and irrationally assigned local learning workloads, SemiFL encounters excessive latency. To overcome the challenges, we propose a retransmission-based over-the-air computation mechanism to facilitate model aggregation and data mixing over quasi-static channels. The closed-form probability of successful aggregation is derived, while the communication latency is modeled based on the Pascal distribution. Further, we establish an optimality gap to characterize the convergence performance of SemiFL, wherein the minimum number of iterations for attaining a specific local target accuracy is identified. Next, a joint resource allocation and local target accuracy assignment problem is formulated to minimize the latency of each round, subject to the decay rate, central processing unit (CPU) frequency, and transmit power. To address this non-convex problem, we develop an algorithm using the closed-form solutions for the normalizing factors and CPU frequencies. Simulation results on two real-world datasets confirm the superiority of SemiFL over benchmarks in terms of latency and learning performance.

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MPPT Optimization using Ant Colony Algorithm: Solar PV applications

MPPT Optimization using Ant Colony Algorithm: Solar PV appli...

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International Conference on Sciences and Techniques of Automatic control and Computer engineering (STA)

作者： Youssef Dhieb Mohamed Yaich Mounir Bouzguenda Moez Ghariani Laboratory of Advanced Electronic System and Sustainable Energy (ESSE) Sfax University National School of Electronics and Telecommunications Sfax Tunisia Electrical Engineering Department College of Engineering King Faisal University AlAhsa Saudi Arabia

ISBN: (纸本)9781665482622

Solar photovoltaic systems' power output depends on the local solar radiation levels among other prevailing climatic parameters such as temperature, dust, and humidity. In fact, the same radiation levels at low temperatures would increase the solar panel output compared to higher ambient temperatures. In this case, the voltage, the current, and the power levels would fluctuate accordingly. Among the three quantities, the voltage and current levels are monitored and often controlled to give regulated voltage and optimal power at the same time. To do so, various techniques are used to enhance the efficiency of solar PV by means of maximum power point tracking (MPPT). The main aim is to set the PV array to operate at optimal power or close to it even when environmental conditions fluctuate. The conventional incremental conductance (INC) algorithm is one of the MPPT approaches, but its fixed step size response speed is low under rapidly changing in the solar irradiation level and or load. To improve response time, a new MPPT technique using the Ant Colony (ACO) approach for photovoltaic (PV) systems is developed and discussed in this work. Artificial ants are released into the problem space to forage, and those that discover a better source of food are kept while the number of ants that are unable to successfully forage is deleted.

关键词： Maximum power point trackers Temperature sensors Temperature measurement Temperature dependence Radiation effects Regulators Time factors

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Experimental Study on the Operational Characteristics and Coupling Relationships of Burners, Heat Exchangers, and Reformers in a Kerosene Solid Oxide Fuel Cell

SSRN

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SSRN 2024年

关键词： Methane

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems Process control Artificial neural networks control systems Approximation error

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Distributed Shadow Dynamic Consensus Optimization Framework for Power Market with Parameterization Bidding Strategy of GENCOs

Distributed Shadow Dynamic Consensus Optimization Framework ...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Liying Yu Peng Wang Zhe Chen Dewen Li Ning Li Rachid Cherkaoui 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 School of Electricity and Electronic Engineering École Polytechnique Fédérale de Lausanne Switzerland

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. First, each sub-group is defined according to the communication principle. The global resource allocation can be solved by introducing the local shadow power demand. Then, for the general market clearing optimization problem, a distributed dynamic consensus algorithm is presented to estimate the general equality constraint. The optimal locational marginal price (LMP) and power allocation in each sub-group are obtained in this step. Finally, the optimal solution is corrected when considering the transmission constraint. A theoretical analysis of the DSDC framework is provided. The algorithm and model are tested by the IEEE 30-bus test system to support the proposed DSDC framework.

关键词： Power demand Heuristic algorithms ISO Consensus algorithm Dynamic scheduling Power markets Smart grids

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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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