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2 results

  • Comparative analysis of energy management techniques for a small unmanned aerial vehicle powered by solar cells

  • Selin Engin, Hasan Cinar, İlyas Kandemir
  • Journal:
    The Aeronautical Journal , First View
    Published online by Cambridge University Press:
    11 May 2026, pp. 1-25
    • Article
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  • In this study, a hybrid propulsion-powered small fixed-wing unmanned aerial vehicle (UAV) was designed to enhance endurance using solar energy. The UAV, a solar-powered vertical take-off and landing (VTOL) with a 1.8 m wingspan and a take-off mass of 3.3 kg, was equipped with a propulsion system comprising solar cells, a battery, a supercapacitor and a DC/DC converter, which was modelled in MATLAB/Simulink to evaluate energy management strategies. To optimise energy utilisation, fuzzy logic (FL), equivalent consumption minimisation strategy (ECMS) and quantum particle swarm optimisation (QPSO) algorithms were implemented. Notably, the QPSO algorithm was integrated into the solar energy management system for the first time. Optimisation results indicate that the QPSO algorithm harnesses solar energy more rapidly and efficiently than other strategies, significantly improving the UAV’s endurance. The time required for the QPSO algorithm to reach maximum power is 1.7948 s and 1.5028 s, shorter than that of the FL and ECMS algorithms, respectively. This result demonstrates that the QPSO algorithm exhibits a fast dynamic response and adapts more efficiently to sudden power demands. Furthermore, considering the time required to reach the maximum power output of 77 W from the solar cell, the corresponding contributions to endurance are calculated as 1.22 h for QPSO, 0.51 h for FL and 0.06 h for ECMS.

  • 13 - User Association in Ultra-dense Networks

  • from Part III - Resource Allocation and Network Management
  • Edited by Haijun Zhang, Jemin Lee, Tony Q. S. Quek, Singapore University of Technology and Design, Chih-Lin I
  • Book:
    Ultra-dense Networks
    Published online:
    12 October 2020
    Print publication:
    26 November 2020, pp 239-258

  • Summary

    In recent years, the overall network data traffic is dramatic increasing,a promising solution is the deployment of ultra dense networks (UDNs) combined with millimeter wave (mmWave) communication technology, which is expected to enhance the overall performance of the network in terms of energy efficiency and load balancing. In this chapter,user association and power allocation inmmWave-based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. This chapter not only establish the system utility optimal function model in the limitations of power and QoS, but also gives an iterative gradient user association and power allocation algorithm to resolve the optimization issue. This algorithm provides a best ratio of convergence and can get a near optimal scheme. In addtion, through utilizing Lagrangian dual decomposition, the dual optimization issue is disintegrated to two sub-problems, we can resolve them respectively. The simulation datum indicate that our method is effective.