基于无模型增量式强化学习的带臂无人机悬停稳定控制方法

• Format: Patent
• Language: Chinese
• Published: 25.03.2025
• Subjects: CONTROL OR REGULATING SYSTEMS IN GENERAL; CONTROLLING; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS; PHYSICS; REGULATING

本发明提供了基于无模型增量式强化学习的带臂无人机悬停稳定控制方法，包括：建立无人机和带载机械臂模型，建立深度强化学习算法框架，初始化神经网络策略，设计分层奖励函数，采集无人机和带载机械臂状态信息，策略神经网络根据采集的状态信息输出增稳控制指令，与PID控制器输出的控制指令进行带参数的权值叠加，得到融合控制律，再将融合控制律输入无人机和带载机械臂模型，解算下一状态信息，利用下一状态信息计算该状态的奖励值，并结合相应动作反馈给深度强化学习算法框架，运行分布式近端优化策略输出策略梯度，计算策略的梯度下降，利用梯度下降更新神经网络，完成策略的更新。本发明能够实时给予指导信息，提升训练效率。 The invention provides an armed unmanned aerial vehicle hovering stability control method based on model-free incremental reinforcement learning, and the method comprises the steps: building an unmanned aerial vehicle and on-load mechanical arm model, building a deep reinforcement learning algorithm framework, initializing a neural network strategy, designing a layered reward function, and collecting the state information of the unmanned aerial vehicle and the on-load mechanical arm. The strategy neural network outputs a stability augmentation control instruction according to the collected state information, the stability augmentation control instruction and a control instruction output by the PID controller are subject