【摘 要】
：
针对空间漂浮基座机械臂抓捕非合作目标控制所基于的多体系统动力学模型难以精确建立的问题,基于深度强化学习算法的控制策略,提出一种深度确定性规则策略。通过将机械臂关节
【机 构】
：
哈尔滨工业大学航天工程系,北京空间飞行器总体设计部
【基金项目】
：
国家自然科学基金(U1737207,11672093)
论文部分内容阅读
针对空间漂浮基座机械臂抓捕非合作目标控制所基于的多体系统动力学模型难以精确建立的问题,基于深度强化学习算法的控制策略,提出一种深度确定性规则策略。通过将机械臂关节控制力矩、抓捕距离以及相对速度等参数定义为高维空间向量,并设置对应的奖励函数,驱动目标参数逼近满足抓捕条件的数值范围,在不依赖运动学模型和动力学模型的情况下,实现对非合作目标的快速逼近和抓捕。仿真算例表明:该控制算法可实现对空间漂浮基机械臂的快速抓捕控制,且能有效降低接触碰撞力的大小,提高抓捕成功率。
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