漂浮基空间机器人捕获卫星过程动力学模拟及捕获后混合体运动的RBF神经网络控制

doi:10.7527/S1000-6893.2013.0157

材料工程与机械制造

本期目录 | 过刊浏览 | 高级检索

前一篇 |

漂浮基空间机器人捕获卫星过程动力学模拟及捕获后混合体运动的RBF神经网络控制

梁捷^1,2, 陈力¹

1. 福州大学机械工程及自动化学院, 福建福州 350108;
2. 中国空气动力研究与发展中心, 四川绵阳 621000

收稿日期:2012-05-23 修回日期:2012-08-16 出版日期:2013-04-25 发布日期:2013-04-23
通讯作者: 陈力,Tel.:0591-87893261E-mail:chnle@fzu.edu.cn E-mail:chnle@fzu.edu.cn
作者简介:梁捷男, 博士研究生。主要研究方向: 航天器系统动力学与控制, 飞行器导航、制导与控制, 计算流体力学。 Tel: 0591-87893261 E-mail: myamoy81@sina.com;陈力男, 博士, 教授, 博士生导师。主要研究方向: 多体系统动力学, 航天器系统动力学与控制, 非线性振动及振动控制。 Tel: 0591-87893261 E-mail: chnle@fzu.edu.cn
基金资助:
国家自然科学基金 (11072061,10672040)

Dynamics Modeling for Free-floating Space-based Robot During Satellite Capture and RBF Neural Network Control for Compound Body Stable Movement

LIANG Jie^1,2, CHEN Li¹

1. Department of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;
2. China Aerodynamics Research and Development Center, Mianyang 621000, China

Received:2012-05-23 Revised:2012-08-16 Online:2013-04-25 Published:2013-04-23
Supported by:
National Natural Science Foundation of China (11072061, 10672040)

摘要/Abstract

摘要：

讨论了漂浮基空间机器人在轨捕获目标卫星过程的碰撞动力学建模,以及捕获操作结束后空间机器人与卫星混合体的稳定控制问题。首先采用多刚体动力学建模方法并结合空间机器人捕获目标卫星过程中的碰撞动力学特性,建立了漂浮基空间机器人在轨捕获漂浮卫星过程的动力学模型,并在此基础上计算出完成捕获操作后空间机器人与目标卫星混合体关节的运动速度。然后针对卫星及空间机器人系统惯性参数均是未知的复杂情况,应用上述模型、神经网络控制理论和Lyapunov稳定性理论,设计了空间机器人与卫星混合体在捕获过程碰撞冲击影响下稳定运动的高斯径向基函数神经网络控制方案,以达到对捕获卫星的有效控制。此外,高斯径向基函数神经网络控制方案具有不需要测量和反馈载体位置、移动速度与加速度的显著优点。系统数值仿真证实了上述控制方案的有效性。

关键词: 漂浮基空间机器人, 在轨捕获卫星, 碰撞动力学建模, 稳定运动, 高斯径向基函数, 神经网络

Abstract:

This paper discusses the collision dynamics modeling of a free-floating space-based robot in the process of on-orbit satellite capturing, and it also analyzes the stability control for the compounded body of the space-based robot and target after the capturing operation is completed. First the method of multibody system dynamics modeling is employed in combination with the characteristics of collision dynamics of the space-based robot while it captures the target satellite to set up a dynamics model for the free-floating space-based robot during its on-orbit capture of the floating satellite. Based on it, the movement speed for the compounded body of the space-based robot and target is calculated. In view of the fact that for the inertia parameters of both the satellite and the space-based robot system are unknown, the model and the theory of neural network control and Lyapunov stability theory are used to design a Gaussian radial basis function neural network control scheme for the compounded body to move stably under the influence of collision and impact during the capturing process. Thus, the effective control is realized for capturing a target satellite. The Gaussian radial basis function neural network control scheme has the obvious advantages of requiring no feedback and measurement of the position,velocity,acceleration,attitude angle velocity and attitude angle acceleration of the floating base. Finally, the validity and applicability of the control scheme are manifested by system numerical simulation.

Key words: free-floating space-based robot, on-orbit capture satellite, collision dynamics modeling, stable movement, Gaussian radial basis function, neural networks

中图分类号:

V447
TP242

梁捷, 陈力. 漂浮基空间机器人捕获卫星过程动力学模拟及捕获后混合体运动的RBF神经网络控制[J]. 航空学报, doi: 10.7527/S1000-6893.2013.0157.

LIANG Jie, CHEN Li. Dynamics Modeling for Free-floating Space-based Robot During Satellite Capture and RBF Neural Network Control for Compound Body Stable Movement[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, doi: 10.7527/S1000-6893.2013.0157.

参考文献

[1] Isenberg D R, Kakad Y P. Quaternion based computed-torque and feed-forward tracking controllers for a space robot. Proceedings of the Annual South Eastern Symposium on System Theory, 2010: 232-236.

[2] Abiko S, Hirzinger G. An adaptive control for a free-floating space robot by using inverted chain approach. Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007: 2236-2241.

[3] Gu Y L, Xu Y S. A normal form augmentation approach to adaptive control of space robot systems. Journal of the Dynamics and Control, 1995, 5(3): 275-294.

[4] Guo Y S, Chen L. Adaptive robust control for free-floating space robot system with dual-arms to track desired trajectory in joint space. Journal of System Simulation, 2009, 21(3): 625-633. (in Chinese) 郭益深, 陈力. 双臂空间机器人的自适应鲁棒性联合控制. 系统仿真学报, 2009, 21(3): 625-633.

[5] Nanos K. On the use of free-floating space robots in the presence of angular momentum. Intelligent Service Robotics, 2011, 4(1): 3-15.

[6] Ding X L, Yu Y S. A multi-propeller and multi-function aero-robot and its motion planning of leg-wall-climbing. Acta Aeronautica et Astronautica Sinica, 2010, 31(10): 2075-2086. (in Chinese) 丁希仑, 俞玉树.一种多旋翼多功能空中机器人及其腿式壁面行走运动规划. 航空学报, 2010, 31(10): 2075-2086.

[7] Ge X S, Chen L Q, Lv J. Nonholonomic motion planning of a space manipulator system using genetic algorithm. Journal of Astronautics, 2005, 26(3): 262-266.(in Chinese) 戈新生, 陈立群, 吕杰. 空间机械臂非完整运动规划的遗传算法研究. 宇航学报, 2005, 26(3): 262-266.

[8] Wang C Q, Zhang C L. Dynamic control of a free-floating flexible dual-arm space robotic system. Chinese Journal of Mechanical Engineering, 2007, 43(10): 196-200. (in Chinese) 王从庆, 张承龙.自由浮动柔性双臂空间机器人系统的动力学控制. 机械工程学报, 2007, 43(10): 196-200.

[9] Hong Z D, Yun C, Chen L. Modeling and trajectory tracking control of a free-floating space robot with flexible manipulators. Robot, 2007, 29(1): 92-96. (in Chinese) 洪在地, 贠超, 陈力. 柔性臂漂浮基空间机器人建模与轨迹跟踪控制. 机器人, 2007, 29(1): 92-96.

[10] Li Q, Wang T S. Adaptive mode method in inverse dynamics of a rotating flexible manipulator with high-frequency excitation. Chinese Journal of Space Science, 2008, 28(4): 345-349. (in Chinese) 李青, 王天舒.高频激励下旋转柔性臂逆动力学模态自适应方法研究. 空间科学学报, 2008, 28(4): 345-349.

[11] Liang J, Chen L. Improved nonlinear feedback control for free-floating space-based robot with time-delay based on predictive and approximation of taylor series. Acta Aeronautica et Astronautica Sinica, 2012, 33(1): 163-169. (in Chinese) 梁捷, 陈力.具有时延的漂浮基空间机器人基于泰勒级数预测、逼近的改进非线性反馈控制. 航空学报, 2012, 33(1): 163-169.

[12] Liang J, Chen L. Fuzzy logic adaptive compensation control for dual-arm space robot based on computed torque controller to track desired trajectory in inertia space. Engineering Mechanics, 2010, 27(11): 221-228. (in Chinese) 梁捷, 陈力. 基于标称计算力矩控制器的双臂空间机器人惯性空间轨迹跟踪的模糊自适应补偿控制.工程力学, 2010, 27(11): 221-228.

[13] Pan J, Cai G P. Particle swarm optimizer for optimal locations of actuators of a trussed structure. Engineering Mechanics, 2009, 26(12): 35-39. (in Chinese) 潘继, 蔡国平. 桁架结构作动器优化配置的粒子群算法. 工程力学, 2009, 26(12): 35-39.

[14] Oda M, Kibe K, Yamagata F. ETS-VII, space robot in-orbit experiment satellite. Proceedings of the IEEE International Conference on Robotics and Automation, 1996: 739-744.

[15] Whelan D A. DARPA orbital express program: effecting a revolution in space-based systems. Proceedings of the International Society for Optical Engineering, 2004: 48-56.

[16] Slotin J E, Li W P. On the adaptive control of robot manipulators. Journal of the Robots Research, 1987, 6(3): 49-59.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

漂浮基空间机器人捕获卫星过程动力学模拟及捕获后混合体运动的RBF神经网络控制

Dynamics Modeling for Free-floating Space-based Robot During Satellite Capture and RBF Neural Network Control for Compound Body Stable Movement

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	胡伟, 万文章, 陈谋. 基于神经网络和干扰观测器的UAV自动着舰控制[J]. 航空学报, 2022, 43(S1): 726963-726963.
[2]	陈博, 岳凯, 王如生, 胡明南. 基于学习策略的多速率多传感器融合定位方法[J]. 航空学报, 2022, 43(S1): 726904-726904.
[3]	王宇斐, 刘骁佳, 刘欢, 曹立俊, 罗志强. 孪生神经网络在航天产品电性能测试方面的应用[J]. 航空学报, 2022, 43(S1): 727048-727048.
[4]	王子玲, 熊振宇, 顾祥岐. 可见光与SAR多源遥感图像关联学习算法[J]. 航空学报, 2022, 43(S1): 727239-727239.
[5]	韩淞宇, 邵海东, 姜洪开, 张笑阳. 基于提升卷积神经网络的航空发动机高速轴承智能故障诊断[J]. 航空学报, 2022, 43(9): 625479-625479.
[6]	杨特, 杨智春, 梁舒雅, 康在飞, 贾有. 平稳随机载荷的信号特征提取与深度神经网络识别[J]. 航空学报, 2022, 43(9): 225952-225952.
[7]	刘佳奇, 冯蕴雯, 路成, 薛小锋, 潘维煌. 基于智能神经网络的航空发动机运行安全分析[J]. 航空学报, 2022, 43(9): 625375-625375.
[8]	邵怡韦, 陈嘉宇, 林翠颖, 万程, 葛红娟, 石智龙. 小训练样本下齿轮箱故障诊断:一种基于改进深度森林的方法[J]. 航空学报, 2022, 43(8): 625429-625429.
[9]	熊伟, 朱洪峰, 崔亚奇. 在线学习的循环自适应机动目标跟踪算法[J]. 航空学报, 2022, 43(5): 325250-325250.
[10]	董珍一, 林莉, 雷明凯, 马志远. 基于BPNN的封严涂层孔隙分布均匀性超声表征[J]. 航空学报, 2022, 43(5): 425294-425294.
[11]	王因翰, 范世鹏, 吴广, 王江, 何绍溟. 基于GRU的敌方拦截弹制导律快速辨识方法[J]. 航空学报, 2022, 43(2): 325024-325024.
[12]	奕建苗, 邓枫, 覃宁, 刘学强. 快速预测跨声速流场的深度学习方法[J]. 航空学报, 2022, 43(11): 526747-526747.
[13]	蔡国飙, 张百一, 贺碧蛟, 翁惠焱, 刘立辉. 真空羽流智能化计算[J]. 航空学报, 2022, 43(10): 527352-527352.
[14]	王辉, 贾自凯, 金忍, 林德福, 范军芳, 徐超. 无人机视觉引导对接过程中的协同目标检测[J]. 航空学报, 2022, 43(1): 324854-324854.
[15]	李昂, 聂党民, 温祥西, 韩宝华, 曾裕景. 管制-飞行状态相依网络演化过程[J]. 航空学报, 2021, 42(9): 324726-324726.