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摘要:
通过结合控制障碍函数与神经网络控制器，提出一种无人机安全降落控制策略。对控制障碍函数和无人机动力学模型进行了介绍，为后续的算法设计提供了理论基础。通过水平集方法构造控制障碍函数，并将其与神经网络控制器相结合，提出一种在避障和安全降落过程中均能有效保障无人机安全的控制策略。对所提算法进行仿真实验，验证了所提控制策略在避障和安全降落方面的有效性，展示了无人机在机动能力受限及姿态约束下的安全避障能力。对所提算法的效果进行总结，并对未来研究的方向进行了展望。
Abstract:This article proposes a safe landing control strategy for unmanned aerial vehicle (UAVs) by integrating control barrier functions with neural network controllers. Initially, control barrier functions and UAV’s dynamical models are introduced, providing a theoretical foundation for subsequent algorithm design. Then, a control approach is proposed that uses the level set method to design control barrier functions and combine them with neural network controllers to successfully ensure UAV safety during obstacle avoidance and safe landing. Simulation experiments then validate the effectiveness of the proposed control strategy in obstacle avoidance and safe landing, demonstrating the UAV’s safe obstacle avoidance capabilities under limited maneuverability and attitude constraints. The success of the suggested algorithm is finally summed up, and potential research avenues are examined.
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图 3 神经网络连接结构示意图
Figure 3. Schematic diagram of the neural network connection structure
图 5 静止平台无人机安全降落控制指令
Figure 5. Control commands for safe drone landing for static platform UAV
图 6 静止平台无人机安全降落姿态约束
Figure 6. Attitude constraints for safe drone landing for static platform UAV
图 8 移动平台无人机安全降落控制指令
Figure 8. Control commands for safe drone landing for moving platform UAV
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