高级检索
    漆海霞, 兰玉彬, 杨秀丽, 张铁民, 彭孝东. 无人机电控速度模糊PI双闭环控制仿真研究[J]. 华南农业大学学报, 2016, 37(6): 31-37. DOI: 10.7671/j.issn.1001-411X.2016.06.005
    引用本文: 漆海霞, 兰玉彬, 杨秀丽, 张铁民, 彭孝东. 无人机电控速度模糊PI双闭环控制仿真研究[J]. 华南农业大学学报, 2016, 37(6): 31-37. DOI: 10.7671/j.issn.1001-411X.2016.06.005
    shu
    QI Haixia, LAN Yubin, YANG Xiuli, ZHANG Tiemin, PENG Xiaodong. Unmanned aerial vehicle speed control simulation study based on fuzzy PI double closed loop control[J]. Journal of South China Agricultural University, 2016, 37(6): 31-37. DOI: 10.7671/j.issn.1001-411X.2016.06.005
    Citation: QI Haixia, LAN Yubin, YANG Xiuli, ZHANG Tiemin, PENG Xiaodong. Unmanned aerial vehicle speed control simulation study based on fuzzy PI double closed loop control[J]. Journal of South China Agricultural University, 2016, 37(6): 31-37. DOI: 10.7671/j.issn.1001-411X.2016.06.005
    shu

    无人机电控速度模糊PI双闭环控制仿真研究

    Unmanned aerial vehicle speed control simulation study based on fuzzy PI double closed loop control

      摘要
    • 摘要:
      目的  针对农用无人机作业时，对速度的稳定恒速需求，研究无人机无刷直流电机的速度控制模糊PI闭环算法。
      方法  分析无人机电控系统的结构原理，根据电控系统驱动无刷直流电机的速度控制要求，在Matlab/Simulink环境下，构建电控驱动无刷直流电机系统的仿真模型，采用速度电流双闭环控制策略，其中，速度环使用模糊PI控制器，电流环使用电流滞环控制。设置系统参数, 进行仿真分析，搭建ARM电路仿真板，验证算法的有效性。
      结果  采用模糊PI后，该系统加快了速度响应，减少了系统超调量，提高了系统的抗干扰能力，提高了系统的动态特性和鲁棒性。
      结论  本研究提出的模糊PI控制策略是有效的，可为无人机实际电机控制系统设计和调试提供理论参考。

       

      Abstract:
      Objective Fuzzy PI closed loop control algorithm was studied for constant speed demand in agricultural unmanned aerial vehicle (UAV) operations.
      Method The principle and structure of the UAV electrical control system was analyzed in this paper. According to the requirement of electrical control system for brushless direct current motor (BLDCM) speed adjustment, the simulation model of BLDCM control system was established in Matlab/Simulink software environment. Double closed loop control of speed and current was applied with fuzzy PI speed control and current hysteresis control. Simulation analysis was conducted with defined systematic parameters. The ARM circuit simulation board was built to verify the effectiveness of the algorithm.
      Result Simulation results proved that the BLDCM control system had improved response speed, reduced overshoot, and higher anti-disturbance capacity with fuzzy PI control. In addition, the dynamic behavior and robustness ability of the system were improved as well.
      Conclusion This study proves effectiveness of the fuzzy PI closed loop contriol algorithm and provides theoretical reference for real UAV control system design and debugging.

       

      • HTML全文
      • 参考文献(20)
      • 相关文章
      • 施引文献
    • 资源附件(0)

    /

    返回文章
    返回