| 引用本文: | 朱安,陈力.配置弹簧阻尼装置空间机器人双臂捕获卫星操作缓冲柔顺控制[J].控制理论与应用,2022,39(1):117~129.[点击复制] |
| ZHU An,CHEN Li.Buffer and compliance control of dual-arm space robot capture satellite operation with spring-damper device[J].Control Theory and Technology,2022,39(1):117~129.[点击复制] |
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| 配置弹簧阻尼装置空间机器人双臂捕获卫星操作缓冲柔顺控制 |
| Buffer and compliance control of dual-arm space robot capture satellite operation with spring-damper device |
| 摘要点击 1565 全文点击 458 投稿时间：2020-11-12 修订日期：2021-05-16 |
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| DOI编号 10.7641/CTA.2021.00795 |
| 2022,39(1):117-129 |
| 中文关键词 双臂空间机器人 弹簧阻尼装置 缓冲柔顺控制 模糊小波神经网络 强化学习 |
| 英文关键词 dual-arm space robot spring-damper device buffer and compliance control fuzzy wavelet network reinforcement learning |
| 基金项目 国家自然科学基金项目(11372073), 福建省机器人基础部件与系统集成创新中心专项资金项目资助. |
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| 中文摘要 |
| 为了避免空间机器人双臂捕获卫星操作过程中关节被冲击载荷破坏, 在电机与机械臂之间加入了一种弹
簧阻尼装置(SDD). 该装置不仅能够吸收、消耗冲击能量, 还能配合所设计的缓冲柔顺策略将冲击力矩限在安全范
围内. 首先, 针对捕获前的双臂空间机器人开环系统与目标卫星系统, 分别利用耗散力Lagrange方程法与Newton–
Euler法建立了其动力学模型. 然后, 结合动量定理、速度约束、闭链几何约束及牛顿第三定律, 导出了捕获后的闭
链混合体系统动力学模型. 针对混合体系统的缓冲柔顺控制问题, 提出一种基于模糊小波神经网络的强化学习控
制策略, 该策略通过自适应惩罚单元处理原始强化信号获得二次强化信号, 具有极强的环境适应能力. 最后, 利用
Lyapunov定理证明了系统的稳定性, 数值仿真验证了弹簧阻尼装置的抗冲击性能及所提策略的有效性. |
| 英文摘要 |
| In order to prevent the joints from being damaged by impact load in the process of dual-arm space robot
capture satellite operation, a spring-damper device (SDD) is added between the motor and the manipulator. The device
can not only absorb and attrition the impact energy, but also limit the impact force to a safe range through reasonable
and coordinated design the compliance strategy. Firstly, the dynamic mode of dual-arm space robot open-loop system and
target satellite system before capturing are established by using Lagrange function based on dissipation theory and Newton-
Euler function respectively. After that, combined with momentum theorem, velocity constraints, closed-chain geometric
constraints and Newton’s third law, the closed-chain dynamic model of hybrid system after capture is obtained. For realize
buffer and compliance control of hybrid system, a reinforcement learning control strategy based on fuzzy wavelet network
is proposed. In this strategy, the primitive reinforcement signal be strengthened by adaptive critic element to obtain the
secondary reinforcement signal, and makes controller has strong environmental adaptability. Finally, the stability of the
system is proved by Lyapunov theorem, and the impact resistance of the device and the effectiveness of the proposed
strategy are verified by numerical simulation. |
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