This study addresses leader-follower controllability of signed multi-agent networks with fixed and switching interactions (topologies), respectively, where weighted edges between agents may be positive or negative. In other words, there exist cooperative interactions (corresponding to positive edges) and antagonistic interactions (corresponding to negative edges) between agents in the multi-agent networks depicted by signed graphs. The goal of this study is to find the criteria of controllability for signed multi-agent networks, that is, how exactly do cooperation and antagonism affect controllability? It is proven that the followers of multi-agent networks under structurally balanced conditions can be controlled to the desired states if the leaders are chosen from the same partitioned subset. In addition, a signed multi-agent network with simultaneously structurally balanced conditions can be switching controllable even though each subsystem is uncontrollable. The leaders' selection is also discussed.
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http://dx.doi.org/10.1016/j.isatra.2021.10.007 | DOI Listing |
Sensors (Basel)
December 2024
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Unmanned aerial vehicles (UAVs) furnished with computational servers enable user equipment (UE) to offload complex computational tasks, thereby addressing the limitations of edge computing in remote or resource-constrained environments. The application of value decomposition algorithms for UAV trajectory planning has drawn considerable research attention. However, existing value decomposition algorithms commonly encounter obstacles in effectively associating local observations with the global state of UAV clusters, which hinders their task-solving capabilities and gives rise to reduced task completion rates and prolonged convergence times.
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December 2024
School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China.
To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft-vehicle-airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle-airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron.
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December 2024
School of Computer Science and Engineering, Northeastern University, Shenyang 110000, China.
Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading.
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December 2024
School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510006, China.
This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The consensus error converges to the prescribed performance bounds in fixed-time by an improved performance function and coordinate transformation. Due to the unknown faults in sensors, the system states cannot be gained correctly; therefore, an adaptive compensation strategy is constructed based on the approximation capabilities of neural networks to solve the negative impact of sensor failures.
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December 2024
Department of Informatics, Mathematics and Electronics, 1 Decembrie 1918 University of Alba Iulia, 510009 Alba Iulia, Romania.
Analyzing IoT data is an important challenge in the smart cities domain due to the complexity of network traffic generated by a large number of interconnected devices: smart cameras, light bulbs, motion sensors, voice assistants, and so on. To overcome this issue, a multi-agent system is proposed to deal with all machine learning steps, from preprocessing and labeling data to discovering the most suitable model for the analyzed dataset. This paper shows that dividing the work into different tasks, managed by specialized agents, and evaluating the discovered models by an Expert System Agent leads to better results in the learning process.
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