空间机器人捕获目标后双幂次滑模神经网络补偿控制

doi:10.16182/j.issn1004731x.joss.201710031

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (10): 2475-2482.doi: 10.16182/j.issn1004731x.joss.201710031

空间机器人捕获目标后双幂次滑模神经网络补偿控制

程靖, 陈力

福州大学机械工程及自动化学院,福建省高端装备制造协同创新中心,福州 350116

收稿日期:2015-10-15 发布日期:2020-06-04
作者简介:程靖(1989-),男,江西赣州,博士生,研究方向为空间机器人系统动力学与控制;陈力(1961-),男,江西九江,博士,教授,博导,研究方向为空间机器人动力学与控制、多体系统动力学。
基金资助:
国家自然科学基金(11372073,11072061),福建省工业机器人基础部件技术重大研发平台(2014H21010011)

Two Power Sliding Mode Neural Network Compensation Control for Space Robot after Target Capturing

Cheng Jing, Chen Li

Fujian Provincial Collaborative Innovation Center of High-EndEquipment Manufacturing, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China

Received:2015-10-15 Published:2020-06-04

摘要/Abstract

摘要： 研究了空间机器人系统捕获不确定参数目标时发生碰撞的冲击效应及之后的稳定控制问题。利用多刚体系统理论获得空间机器人及目标动力学模型。利用运动几何关系及机械臂末端与目标物之间力的传递关系,分析了空间机械臂捕获目标的冲击影响。针对完成捕获操作后的联合体系统存在参数不确定及外部扰动的情况,提出了双幂次滑模神经网络方案。利用快速双幂次滑模趋近律保证了系统的收敛速度,运用神经网络逼近系统的参数不确定项及外部扰动,上述控制方案具有抑制抖振的效果。基于李雅普诺夫方法,设计了权值自适应律,证明了系统的全局稳定性。计算机数值仿真实验模拟了碰撞冲击效应,验证了上述控制方案的有效性。

关键词: 漂浮基空间机器人, 捕获目标, 双幂次趋近律, 神经网络

Abstract: The impact analyses of space robot capturing a target and stability control problem in the post-impact process were discussed. The dynamic models of space robot system and target were derived by multi-body theory. The impact effect of rigidcouplingmodel was analyzed by applying geometric relationship and principle of momentum conservation. Atwo power sliding mode neural network control scheme was proposed for the combined system after acquiring with uncertain system parameters and external disturbance. The convergence speed of the control system was guaranteed by applyingtwo power sliding mode reaching raw, and the uncertain part was compensated by using neural network. The proposed control scheme can eliminate the chattering. Based on the Lyapunov method, the weights adaptive law was designed and the stability of the combined system was demonstrated. Computer numerical simulation example simulated the process of collision impact effect and verified the validity of the proposed control scheme.

Key words: free-floating space robot, capture target, twopower reaching raw, neural network

中图分类号:

TP241

程靖, 陈力. 空间机器人捕获目标后双幂次滑模神经网络补偿控制[J]. 系统仿真学报, 2017, 29(10): 2475-2482.

Cheng Jing, Chen Li. Two Power Sliding Mode Neural Network Compensation Control for Space Robot after Target Capturing[J]. Journal of System Simulation, 2017, 29(10): 2475-2482.

参考文献

[1] Diftler M A, Mehling J S, Aabdallah M E, et al.Robonaut2-the first humanoid robot in space[C]// Proceeding of IEEE International Conference on Robotics and Automation, Shanghai, China. USA: IEEE, 2011: 2178-2183.
[2] Pazelli T F, Terra M H, Siqueira A A, et al.Experimental investigation on adaptive robust controller design applied to a free-floating space manipulator[J]. Control Engineering Practice(S0967-0661), 2011, 19(1): 395-408.
[3] Aslanov V, Kruglov G, Yudintsev V.Newton-Euler equations of multibody systems with changing structures for space applications[J]. Acta Astronautica(S0094-5765), 2011, 68(11): 2080-2087.
[4] Nanos K, Papadopoulos E.On cartesian motions with singularities avoidance for free-floating space robots[C]// Proceeding of IEEE International Conference on Robotics and Automation, Saint Paul,America. USA: IEEE, 2012: 5398-5403.
[5] Cheng J, Chen L.Decentralized Adaptive Neural Network Stabilization Control and Vibration Suppression of Flexible Robot Manipulator during Capture a Target[C]// The 66rd International Astronautical Congress, Jerusalem, Israel, 2015.Israel: IAF, 2015.
[6] Boyarko G, Yakimenko O, Romano M.Optimal rendezvous Trajectories of a controlled spacecraft and a tumbling object[J] Journal of Guidance, Control and Dynamics (S0731-5090), 2011, 34(4): 1239-1252.
[7] Abad A F, Ma O, Pham K, et al.A review of space robotics technologies for on-orbit servicing[J]. Progress in Aerospace Sciences (S0376-0421), 2014, 68: 1-26.
[8] 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[J]. 机器人, 2007, 29(1): 92-96.
(Hong Zaidi, YunCao, Chen Li. Modeling and trajectory tracking control of a free-floating space robot with flexible manipulators[J]. Robot(S1002-0446), 2007, 29(1): 92-96.)
[9] 陈志勇, 陈力. 空间机器人增广自适应神经网络补偿控制[J]. 系统仿真学报, 2011, 23(12): 2750-2755.
(Chen Zhiyong, Chen Li.Augmented adaptive neural network compensation control of space-based robot[J]. Journal of System Simulation (S1004-731X), 2011, 23(12): 2750-2755.)
[10] 程靖, 陈力. 不确定空间机器人自适应模糊H∞控制[J]. 载人航天, 2015, 21(6): 564-567.
(Cheng Jing, Chen Li.Adaptive Fuzzy H-infinity Control of Uncertain Space Robot System[J]. Manned Spaceflight, 2015, 21(6): 564-567.)
[11] 王从庆, 吴鹏飞, 周鑫. 基于最小关节力矩优化的自由浮动空间刚柔耦合机械臂混沌动力学建模与控制[J]. 物理学报, 2012, 61(23): 230503.
(Wang Congqing, Wu Pengfei, Zhou Xin. Control and modeling of chaotic dynamics for a free-floating rigid-flexible coupling space manipulator based on minimal joint torque's optimization [J]. Acta Physica Sinica (S1000-3290), 2012, 61(23): 230503.)
[12] Huang P F, Yuan J P, Xu Y S, et al.Approach Trajectory Planning of Space Robot for Impact Minimization[C]//2006 IEEE International Conference on Information Acquisition, August 20-23, 2006, Weihai, Shandong, China. USA: IEEE, 2006: 382-387.
[13] Walker I D.The Use of Kinematic Redundancy in Reducing Impact and Contact Effect in Manipulator[C]//1990 IEEE International Conference on Robotics and Automation, May 13-18, 1990, Cincinnati, OH, USA. USA: IEEE, 1990: 434-439.
[14] 董楸煌, 陈力. 空间机器人捕获目标过程的自适应仿真[J]. 系统仿真学报, 2014, 26(12): 2969-2973.
(Dong Qiuhuang, Chen Li.Adaptive control simulation of space manipulator capturing target[J]. Journal of System Simulation (S1004-731X), 2014, 26(12): 2969-2973.)
[15] Liu J K.Radial basis function (RBF) neural network control for mechanical systems: design, analysis and matlab simulation[M]. Heidelberg, Germany: Springer, 2013.
[16] 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系统的RBF神经网络控制[J]. 航空学报, 2013, 34(4): 970-978.
(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 Aeronautica et Astronautica Sinica(S1000-6893), 2013, 34(4): 970-978.)

空间机器人捕获目标后双幂次滑模神经网络补偿控制

Two Power Sliding Mode Neural Network Compensation Control for Space Robot after Target Capturing

PDF (PC)

可视化

被引次数

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]	张森, 张孟炎, 邵敬平, 普杰信. 基于随机策略搜索的多机三维路径规划方法[J]. 系统仿真学报, 2022, 34(6): 1286-1295.
[2]	张立峰, 王会忍. 基于卷积神经网络及有限元仿真的电容层析成像图像重建[J]. 系统仿真学报, 2022, 34(4): 712-718.
[3]	康旭, 张晓峰. 基于生成对抗神经网络的雷达遥感数据增广方法[J]. 系统仿真学报, 2022, 34(4): 920-927.
[4]	周思锦, 陈棣成, 涂耿, 姜大志. 基于个性化和记忆机制的多模态情感计算模型[J]. 系统仿真学报, 2022, 34(4): 745-758.
[5]	孙晓安, 栾小丽, 刘飞. 基于智能优化灰色模型的电子固废预测[J]. 系统仿真学报, 2022, 34(3): 536-542.
[6]	魏娟, 游磊, 郭阳勇, 唐志海. 基于小波神经网络的多楼层疏散模型[J]. 系统仿真学报, 2022, 34(2): 269-277.
[7]	敖邦乾, 杨莎, 令狐金卿, 叶振环. 基于级联神经网络疲劳驾驶检测系统设计[J]. 系统仿真学报, 2022, 34(2): 323-333.
[8]	魏腾飞, 潘庭龙. 基于改进PSO优化LSTM网络的短期电力负荷预测[J]. 系统仿真学报, 2021, 33(8): 1866-1874.
[9]	赵瑛, 陆耀, 张健, 梁启弟, 龙炜. 基于深度神经网络的多视角人体动作识别[J]. 系统仿真学报, 2021, 33(5): 1019-1030.
[10]	刘晓琳, 吴竟祎. 飞机舵机电液加载系统控制补偿器设计与仿真[J]. 系统仿真学报, 2021, 33(5): 1031-1038.
[11]	于广宇, 董学平, 王祥民, 甘敏. 弹性网下基于LSTM的分解炉出口温度预测[J]. 系统仿真学报, 2021, 33(5): 1078-1085.
[12]	方群, 陈绪凯, 何昕, 祝玉军, 刘毅杨, 方阳阳, 李贺举. 基于SWIPT无线协作网络的中继选择算法研究[J]. 系统仿真学报, 2021, 33(4): 801-808.
[13]	仝卫国, 庞雪纯, 朱赓宏. 基于卷积神经网络的气液两相流流型识别方法[J]. 系统仿真学报, 2021, 33(4): 883-891.
[14]	冯新扬, 邵超. 跨卷积网络特征融合的SAR图像目标识别[J]. 系统仿真学报, 2021, 33(3): 554-561.
[15]	马立新, 朱勇杰, 季乐延. 神经网络优化的无感永磁同步电机控制系统[J]. 系统仿真学报, 2021, 33(3): 622-630.