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

doi:10.16182/j.issn1004731x.joss.201710031

Abstract

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

CLC Number:

TP241

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.

References

[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.)