Stochastic Drift Counteraction Optimal Control of a Fuel Cell-Powered Small Unmanned Aerial Vehicle
Stochastic Drift Counteraction Optimal Control of a Fuel Cell-Powered Small Unmanned Aerial Vehicle
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DOI：
10.3390/en14051304
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发表时间：
2021-02
期刊：
影响因子：
3.2
通讯作者：
Jiadi Zhang;I. Kolmanovsky;M. Amini
中科院分区：
文献类型：
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作者：
Jiadi Zhang;I. Kolmanovsky;M. Amini
本文研究了随机环境下燃料电池混合动力小型无人机（sUAV）续航时间（飞行时间）最大化的最优功率管理。利用随机漂移抵消最优控制来获得用于功率管理的最优策略，该策略协调燃料电池和电池的操作以最大化预期飞行时间，同时考虑燃料电池功率输出的变化率的限制和燃料电池效率的方向依赖性。所提出的功率管理策略考虑了使命期间螺旋桨功率和爬升角转变的已知统计数据，但不需要精确预览它们的时间历史。最优控制策略是使用Cython中实现的值迭代离线生成的，与MATLAB相比，速度提高了一个数量级。它还表明，价值迭代可以进一步加快使用折扣因子，但在性能下降的成本。一个1.5公斤的sUAV的仿真结果报告，说明在飞机机动，包括收费公路的电池充电状态（SOC）轨迹的燃料电池和电池之间的最佳协调。由于燃料电池不能支持功率输出的快速变化，如果从低初始SOC值开始，则示出了将电池充电到收费公路值的最优策略。如果从高SOC值开始，则使用电池能量直到达到SOC的收费公路值，进一步放电延迟到飞行中的稍后。对于考虑的特定场景和模拟sUAV参数，结果表明飞行时间长达2.7小时的能力。
This paper investigates optimal power management of a fuel cell hybrid small unmanned aerial vehicle (sUAV) from the perspective of endurance (time of flight) maximization in a stochastic environment. Stochastic drift counteraction optimal control is exploited to obtain an optimal policy for power management that coordinates the operation of the fuel cell and battery to maximize the expected flight time while accounting for the limits on the rate of change of fuel cell power output and the orientation dependence of fuel cell efficiency. The proposed power management strategy accounts for known statistics in transitions of propeller power and climb angle during the mission, but does not require the exact preview of their time histories. The optimal control policy is generated offline using value iterations implemented in Cython, demonstrating an order of magnitude speedup as compared to MATLAB. It is also shown that the value iterations can be further sped up using a discount factor, but at the cost of decreased performance. Simulation results for a 1.5 kg sUAV are reported that illustrate the optimal coordination between the fuel cell and the battery during aircraft maneuvers, including a turnpike in the battery state of charge (SOC) trajectory. As the fuel cell is not able to support fast changes in power output, the optimal policy is shown to charge the battery to the turnpike value if starting from a low initial SOC value. If starting from a high SOC value, the battery energy is used till a turnpike value of the SOC is reached with further discharge delayed to later in the flight. For the specific scenarios and simulated sUAV parameters considered, the results indicate the capability of up to 2.7 h of flight time.