电机-变距螺旋桨动力系统功率优化控制

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。

Power optimization and control of motor variable-pitch propeller propulsion system

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.