电动螺旋桨无人机应用越来越普及，但普遍续航时间较短，提高电动力系统效率、降低功率消耗是提高航时的主要措施。电机-变距螺旋桨动力系统（以下简称变距电动力系统）可同时改变转速、桨距两个量，存在桨距和转速的最佳组合，使系统功率最小。相比电机-定距螺旋桨动力系统，其在耗能方面具有特殊优势，但如何达到最小功率点，目前研究较少。针对上述问题，为提高计算效率，便于控制研究工作的开展，首先基于改进天牛须算法的BP神经网络训练得到变距电动力系统的神经网络代理模型。接着提出了一种变距电动力系统功率优化控制策略：在一定入流速度、拉力需求下，基于自适应扩展卡尔曼滤波-牛顿法实时优化桨距，并在一定桨距下利用模糊PID控制系统转速以达目标拉力，实现目标拉力需求下的最小功率控制。仿真验证结果表明，提出的功率优化控制策略鲁棒性更强、优化速度更快、收敛效果更好。
The application of unmanned electric propeller aerial vehicles is gaining increasing popularity. However, the endurance time of these aerial vehicles is short, which partly results from the low efficiency and high power consumption of the electric propulsion system. Improving the efficiency and reducing the power consumption are therefore the main measures to extend the endurance time. The motor-variable pitch propeller propulsion system (variable pitch electric propulsion system) can simultaneously change the rotational speed and the pitch to reach an optimal pitch, enabling the minimum power consumption of the system. Compared with motor-fixed pitch propeller system, this system has particular advantages in energy consumption, yet how to achieve the minimum power point is less studied at present. To solve this problem, the back propagation neural network training method combined with the improved beetle antennae search algorithm is used to establish the neural network agent model of the variable-pitch propulsion system. Then a power optimization and control strategy of the system is proposed. With certain inflow speed and force demand, the Newton’s method based on an improved extended Kalman filter algorithm is adopted to optimize the pitch. At a certain pitch point, the fuzzy PID control is applied to adjust the rotational speed in order to produce the target force, finally achieving the minimum power as well as the target force. Simulation results show that this strategy has stronger robustness, faster optimization speed and better convergence effect.
[1] HONKAVAARA E, SAARI H, KAIVOSOJA J. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture[J]. Remote Sensing, 2013, 5(10):5006-5039.
[2] OSTLER J N, BOWMAN W J, SNYDER D O, et al. Performance flight testing of small, electric powered unmanned aerial vehicles[J]. International Journal of Micro Air Vehicles, 2009, 1(3):155-171.
[3] DUNBABIN M, MARQUES L. Robots for environmental monitoring:Significant advancements and applications[J]. IEEE Robotics & Automation Magazine, 2012, 19(1):24-39.
[4] LI Z, LIU Y, HAYWARD R, et al. Knowledge-based power line detection for UAV surveillance and inspection systems[C]//2008 23rd International Conference Image and Vision Computing New Zealand. Piscataway:IEEE Press, 2008:1-6.
[5] CUTLER M, URE N K, MICHINI B, et al. Comparison of fixed and variable pitch actuators for agile quadrotors[C]//AIAA Guidance, Navigation, and Control Conference. Reston:AIAA, 2011:6406.
[6] DUAN D, ZHAO H, PENG M, et al. Research on optimal design method of tilt-rotor electric propulsion system[C]//Asia-Pacific International Symposium on Aerospace Technology. Berlin:Springer, 2019:1108-1119.
[7] CAZENAVE T, PAKMEHR M, FERON E. Peak-seeking control of a DC motor driving a variable pitch propeller[C]//AIAA Guidance, Navigation, and Control Conference. Reston:AIAA, 2011:6255.
[8] CAZENAVE T. Peak-seeking control of propulsion systems[D]. Atlanta:Georgia Institute of Technology, 2012:4-20.
[9] COHEN R, MICULESCU D, REILLEY K. Online performance optimization of a DC motor driving a variable pitch propeller[EB/OL]. (2013-10-01)[2020-04-29]. https://arxiv.org/abs/1310.0133.
[10] FRESK E, NIKOLAKOPOULOS G. Experimental model derivation and control of a variable pitch propeller equipped quadrotor[C]//2014 IEEE Conference on Control Applications (CCA). Piscataway:IEEE Press, 2014:723-729.
[11] SHENG S, SUN C. Control and optimization of a variable-pitch quadrotor with minimum power consumption[J]. Energies, 2016, 9(4):232.
[12] HENDERSON T, PAPANIKOLOPOULOS N. Power-minimizing control of a variable-pitch propulsion system for versatile unmanned aerial vehicles[C]//2019 International Conference on Robotics and Automation (ICRA). Piscataway:IEEE Press, 2019:4148-4153.
[13] LARMINIE J, LOWRY J. Electric vehicle technology explained[M]. Manhattan:John Wiley & Sons, 2012:145-185.
[14] MCDONALD R A. Electric propulsion modeling for conceptual aircraft design[C]//52nd Aerospace Sciences Meeting. Reston:AIAA, 2014:0536.
[15] MCDONALD R A. Optimal propeller pitch scheduling and propeller——airframe matching for conceptual design[C]//15th AIAA Aviation Technology, Integration, and Operations Conference. Reston:AIAA, 2015:3190.
[16] MCDONALD R A. Modeling of electric motor driven variable pitch propellers for conceptual aircraft design[C]//54th AIAA Aerospace Sciences Meeting. Reston:AIAA, 2016:1025.
[17] 刘沛清. 空气螺旋桨理论及其应用[M]. 北京:北京航空航天大学出版社, 2006:55-100. LIU P Q. Theory and application of airscrew[M]. Beijing:Beihang University Press, 2006:55-100(in Chinese).
[18] TJHAI C. Developing stochastic model of thrust and flight dynamics for small UAVs[D]. Twin Cities:University of Minnesota Twin Cities, 2013:4-48.
[19] 王甜甜, 刘强. 基于BAS-BP模型的风暴潮灾害损失预测[J]. 海洋环境科学, 2018, 37(3):457-463. WANG T T, LIU Q. The assessment of storm surge disaster loss based on BAS-BP model[J]. Marine Environmental Science, 2018, 37(3):457-463(in Chinese).
[20] 赵耀, 熊智, 田世伟, 等. 基于SAR图像匹配结果可信度评价的INS/SAR自适应Kalman滤波算法[J]. 航空学报, 2019, 40(8):322850. ZHAO Y, XIONG Z, TIAN S W, et al. INS/SAR adaptive Kalman filtering algorithm based on credibility evaluation of SAR image matching results[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(8):322850(in Chinese).
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