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Sequential generation of self-starting diverse operations in all-fiber laser based on thulium-doped fiber saturable absorber

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Chinese Physics B 2022年第6期31卷 87-90页

作者： Pei Zhang Kaharudin Dimyati Bilal Nizamani Mustafa M.Najm S.W.Harun School of Electrical and Information Engineering Huaihua UniversityHuaihua 418008China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua 418008China Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Advanced Technology and Multidiscipline Airlangga UniversitySurabaya 60115Indonesia Institute of Computer Science and Digital Innovation UCSI UniversityKuala LumpurMalaysia

Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** central wavelengths of Q-switching,Q-switched mode-locking and mode-locking operation modes are 1569.7 nm,1570.9 nm,and 1572 nm,*** mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 *** results validate the capability of generating soliton pulse by doped fiber saturable ***,the proposed fiber laser is beneficial to the applications of optical communication and signal processing system.

关键词： all-fiber laser Q-switched mode-locking dark soliton fiber saturable absorber

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Minimum Time Escape from a Circular Region of a Dubins Car

Minimum Time Escape from a Circular Region of a Dubins Car

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Isaac E. Weintraub Alexander Von Moll Meir N. Pachter Control Science Center Air Force Research Laboratory Wright-Patterson AFB OH Electrical and Computer Engineering Air Force Institute of Technology Wright-Patterson AFB OH

A turn-constrained evader strives to escape a circular region in minimum time.

ISBN: (数字)9798350367621

ISBN: (纸本)9798350367638

A turn-constrained evader strives to escape a circular region in minimum time.

关键词： Geometry Aerospace electronics Automobiles

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Decentralized control of Multi-Agent Systems Under Acyclic Spatio-Temporal Task Dependencies

Decentralized Control of Multi-Agent Systems Under Acyclic S...

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IEEE Conference on Decision and control

作者： Gregorio Marchesini Siyuan Liu Lars Lindemann Dimos V. Dimarogonas Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We introduce a novel distributed sampled-data control method tailored for heterogeneous multi-agent systems under a global spatio-temporal task with acyclic dependencies. Specifically, we consider the global task as a conjunction of independent and collaborative tasks, defined over the absolute and relative states of agent pairs. Task dependencies in this form are then represented by a task graph, which we assume to be acyclic. From the given task graph, we provide an algorithmic approach to define a distributed sampled-data controller prioritizing the fulfilment of collaborative tasks as the primary objective, while fulfilling independent tasks unless they conflict with collaborative ones. Moreover, communication maintenance among collaborating agents is seamlessly enforced within the proposed control framework. A numerical simulation is provided to showcase the potential of our control framework.

关键词： Scalability Decentralized control Collaboration Numerical simulation Robustness Maintenance Multi-agent systems

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Communication-Constrained STL Task Decomposition Through Convex Optimization

Communication-Constrained STL Task Decomposition Through Con...

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American control Conference (ACC)

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We propose a method to decompose signal temporal logic tasks for multi-agent systems under communication constraints. Specifically, given a task graph representing task dependencies among couples of agents in the system, we propose to decompose tasks assigned to couples of agents not connected in the communication graph by a set of sub-tasks assigned to couples of communicating agents over the communication graph. To this end, we parameterize the predicates' level set of tasks to be decomposed as hyper-rectangles with parametric centres and dimensions. Convex optimization is then leveraged to find optimal parameters maximising the volume of the predicate's level sets. Moreover, a formal treatment of conflicting conjunctions of formulas in the considered STL fragment is introduced, including sufficient conditions to avoid the insurgence of such conflicts in the final decomposition.

关键词： Sufficient conditions Level set Convex functions Task analysis Multi-agent systems

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Heterogeneous Neural Network: Hidden Firing Patterns, Phase Synchronization and Multistability

Heterogeneous Neural Network: Hidden Firing Patterns, Phase ...

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Memristive Computing and Applications (ICMCA), IEEE International Conference on

作者： Lili Huang Hongyan Zang Tengfei Lei Na Li Haiyan Fu Aidi Liu Collaborative Innovation Center of Memristive Computing Application(CICCA) Qilu Institute of Technology Jinan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China school of computer and information engineering Qilu Institute of Technology Jinan China

In this paper, a novel heterogeneous neural network is proposed by coupling improved memristor 2D HR neuron and 1D HNN neuron with locally active memristor. The effect of coupling intensity on synchronization behavior is analyzed. The results show that the increase in coupling intensity will lead to phase synchronization of the system. The coexistence of the firing pattern caused by the initial enhancement of the memristor is quantitatively analyzed and numerically simulated by introducing a multistable auto memristor. The results show that the newly constructed heterogeneous neural network exhibits an attractor bias pattern that enhances the initial migration behavior of the memristor, and these patterns have extreme multi-stability.

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Model-based Policy Optimization using Symbolic World Model

Model-based Policy Optimization using Symbolic World Model

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Andrey Gorodetskiy Konstantin Mironov Aleksandr Panov Center of Cognitive Modeling Moscow Institute of Physics and Technology Dolgoprudny Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Artificial Intelligence Research Institute Moscow Russia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The application of learning-based control methods in robotics presents significant challenges. One is that model-free reinforcement learning algorithms use observation data with low sample efficiency. To address this challenge, a prevalent approach is model-based reinforcement learning, which involves employing an environment dynamics model. We suggest approximating transition dynamics with symbolic expressions, which are generated via symbolic regression. Approximation of a mechanical system with a symbolic model has fewer parameters than approximation with neural networks, which can potentially lead to higher accuracy and quality of extrapolation. We use a symbolic dynamics model to generate trajectories in model-based policy optimization to improve the sample efficiency of the learning algorithm. We evaluate our approach across various tasks within simulated environments. Our method demonstrates superior sample efficiency in these tasks compared to model-free and model-based baseline methods.

关键词： Heuristic algorithms Robot kinematics Neural networks Reinforcement learning Predictive models Transformers Approximation algorithms Inference algorithms Trajectory Optimization

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Trajectory control and Fair Communications for Multi-UAV Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Tarekegn, Getaneh Berie Tesfaw, Belayneh Abebe Juang, Rong-Terng Saha, Dola Tarekegn, Robel Berie Lin, Hsin-Piao Tai, Li-Chia Nycu Department of Electrical and Computer Engineering 1001 University Road Hsinchu300 Taiwan Nycu Institute of Electrical and Control Engineering 1001 University Road Hsinchu300 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Institute of Aerospace and System Engineering Taipei10608 Taiwan University at Albany Suny Department of Electrical and Computer Engineering AlbanyNY12222 United States

Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user's link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents' privacy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Model Predictive control for Autonomous Driving: Comparing Kinematic and Dynamic Models of Tractor-Trailer Systems

Model Predictive Control for Autonomous Driving: Comparing K...

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International Conference on Intelligent Transportation

作者： Gustav Vallinder Jonas Mårtensson Pedro F. Lima Sriharsha Bhat Scania CV AB Södertälje Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Investigations of kinematic and dynamic models of tractor-trailer systems have historically been performed for stability analysis or state estimation. In this work, we present and evaluate kinematic and dynamic tractor-trailer models for model predictive control (MPC). We show in open-loop simulations that a kinematic and a dynamic model are equivalent at low speeds and short discretization time steps. A zero speed singularity and stiff dynamics prevents the usage of the dynamic model in control design, where discretization time steps are longer. A method of discretization is proposed to resolve the low speed feasibility of the dynamic model. In closed-loop simulations, the real-time applicability of the kinematic and dynamic models in a nonlinear MPC is verified.

关键词： control design Optimal control Kinematics Predictive models Real-time systems Stability analysis State estimation Intelligent transportation systems Autonomous vehicles Predictive control

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A Rolling Horizon Game Considering Network Effect in Cluster Forming for Dynamic Resilient Multiagent Systems

arXiv

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arXiv 2023年

作者： Nugraha, Yurid Cetinkaya, Ahmet Hayakawa, Tomohisa Ishii, Hideaki Zhu, Quanyan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Department of Functional Control Systems Shibaura Institute of Technology Tokyo135-8548 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

A two-player game-theoretic problem on resilient graphs in a multiagent consensus setting is formulated. An attacker is capable to disable some of the edges of the network with the objective to divide the agents into clusters by emitting jamming signals while, in response, the defender recovers some of the edges by increasing the transmission power for the communication signals. Specifically, we consider repeated games between the attacker and the defender where the optimal strategies for the two players are derived in a rolling horizon fashion based on utility functions that take both the agents’ states and the sizes of clusters (known as network effect) into account. The players’ actions at each discrete-time step are constrained by their energy for transmissions of the signals, with a less strict constraint for the attacker. Necessary conditions and sufficient conditions of agent consensus are derived, which are influenced by the energy constraints. The number of clusters of agents at infinite time in the face of attacks and recoveries are also characterized. Simulation results are provided to demonstrate the effects of players’ actions on the cluster forming and to illustrate the players’ performance for different horizon parameters. © 2023, CC BY.

关键词： Game theory

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Maximizing social power in multiple independent Friedkin-Johnsen models

Maximizing social power in multiple independent Friedkin-Joh...

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European control Conference (ECC)

作者： Lingfei Wang Yu Xing Claudio Altafini Karl H. Johansson Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering Division of Automatic Control Linköping University Linköping Sweden

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the problem of maximizing social power for a group of agents, who participate in multiple meetings described by independent Friedkin-Johnsen models. A strategic game is obtained, in which the action of each agent (or player) is her stubbornness over all the meetings, and the payoff is her social power on average. It is proved that, for all but some strategy profiles on the boundary of the feasible action set, each agent's best response is the solution of a convex optimization problem. Furthermore, even with the non-convexity on boundary profiles, if the underlying networks are given by a fixed complete graph, the game has a unique Nash equilibrium. For this case, the best response of each agent is analytically characterized, and is achieved in finite time by a proposed algorithm.

关键词： Network topology Europe Games Nash equilibrium Cost function Convex functions

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