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Parameters concurrent learning and reactionless control in post-capture of unknown targets by space manipulators

  • Lijun Zong
  • Jianjun LuoEmail author
  • Mingming Wang
  • Jianping Yuan
Original Paper
  • 17 Downloads

Abstract

This paper studies parameters identification and minimizing base disturbances problems after the space manipulator capturing an unknown target. A concurrent learning algorithm that concurrently uses past motion data points and instantaneous motion data of the system is proposed for the parameters identification. Given a condition for selecting the used past data points as well as a scaling technique to make the parameters have the same magnitude, the concurrent learning algorithm guarantees that parameters identification errors can globally converge to zero at an exponential rate and without the need for satisfying the persistent excitation (PE) condition. An adaptive reactionless control method is proposed based on the passivity theorem and Task-priority method, which ensures that the base attitude is stationary and joint motions satisfy the limits during the system generating excitation motions for the parameters identification. Simulation results verify the effectiveness of the proposed method.

Keywords

Space manipulators Parameters identification Adaptive control Concurrent learning Reactionless control 

Notes

Acknowledgements

This work was supported by the Major Program of National Natural Science Foundation of China under Grant Nos. 61690210 and 61690211, the National Natural Science Foundation of China under Grant No. 61603304, and the Fundamental Research Funds for the Central Universities.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Flores-Abad, A., Ma, O., Pham, K., Ulrich, S.: A review of space robotics technologies for on-orbit servicing. Prog. Aerosp. Sci. 68, 1–26 (2014)CrossRefGoogle Scholar
  2. 2.
    Aghili, F., Parsa, K.: Motion and parameter estimation of space objects using laser-vision data. J. Guid. Control Dyn. 32(2), 538–550 (2009)CrossRefGoogle Scholar
  3. 3.
    Aghili, F., Kuryllo, M., Okouneva, G., English, C.: Fault-tolerant position/attitude estimation of free-floating space objects using a laser range sensor. IEEE Sensors J. 11(1), 176–185 (2011)CrossRefGoogle Scholar
  4. 4.
    Hillenbrand, U., Lampariello, R.: Motion and parameter estimation of a free-floating space object from range data for motion prediction. In: 8th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (2005)Google Scholar
  5. 5.
    Wang, M., Luo, J., Yuan, J., Walter, U.: An integrated control scheme for space robot after capturing non-cooperative target. Acta Astronautica 147, 350–363 (2018)CrossRefGoogle Scholar
  6. 6.
    Chu, Z., Ma, Y., Hou, Y., Wang, F.: Inertial parameter identification using contact force information for an unknown object captured by a space manipulator. Acta Astronautica 131, 69–82 (2017)CrossRefGoogle Scholar
  7. 7.
    Murotsu, Y., Senda, K., Ozaki, M., Tsujio, S.: Parameter identification of unknown object handled by free-flying space robot. J. Guid. Control Dyn. 17(3), 488–494 (1994)CrossRefzbMATHGoogle Scholar
  8. 8.
    Abiko, S., Hirzinger, G.: On-line parameter adaptation for a momentum control in the post-grasping of a tumbling target with model uncertainty. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. IROS 2007, pp. 847–852. IEEE (2007)Google Scholar
  9. 9.
    Nguyen-Huynh, T.C., Sharf, I.: Adaptive reactionless motion and parameter identification in postcapture of space debris. J. Guid. Control Dyn. 36(2), 404–414 (2013)CrossRefGoogle Scholar
  10. 10.
    Christidi-Loumpasefski, O.O., Nanos, K., Papadopoulos, E.: On parameter estimation of space manipulator systems using the angular momentum conservation. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5453–5458. IEEE (2017)Google Scholar
  11. 11.
    Xu, W., Hu, Z., Zhang, Y., Liang, B.: On-orbit identifying the inertia parameters of space robotic systems using simple equivalent dynamics. Acta Astronautica 132, 131–142 (2017)CrossRefGoogle Scholar
  12. 12.
    Karl, J., Björn, W.: Adaptive Control, 2nd edn. Addison-Weseley, Readings (1995)Google Scholar
  13. 13.
    Chowdhary, G., Johnson, E.: Concurrent learning for convergence in adaptive control without persistency of excitation. In: 2010 49th IEEE Conference on Decision and Control (CDC), pp. 3674–3679. IEEE (2010)Google Scholar
  14. 14.
    Chowdhary, G., Johnson, E.: A singular value maximizing data recording algorithm for concurrent learning. In: 2011 American Control Conference (ACC), pp. 3547–3552. IEEE (2011)Google Scholar
  15. 15.
    Chowdhary, G., Mühlegg, M., How, J.P., Holzapfel, F.: Concurrent learning adaptive model predictive control. Advances in Aerospace Guidance. In: Navigation and Control, pp. 29–47. Springer, Berlin (2013)Google Scholar
  16. 16.
    Kamalapurkar, R., Andrews, L., Walters, P., Dixon, W.E.: Model-based reinforcement learning for infinite-horizon approximate optimal tracking. IEEE Trans Neural Netw. Learn. Syst. 28(3), 753–758 (2017)CrossRefGoogle Scholar
  17. 17.
    Vamvoudakis, K.G., Miranda, M.F., Hespanha, J.P.: Asymptotically stable adaptive-optimal control algorithm with saturating actuators and relaxed persistence of excitation. IEEE Trans. Neural Netw. Learning Syst. 27(11), 2386–2398 (2016)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kamalapurkar, R., Rosenfeld, J.A., Dixon, W.E.: Efficient model-based reinforcement learning for approximate online optimal control. Automatica 74, 247–258 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Zhao, D., Zhang, Q., Wang, D., Zhu, Y.: Experience replay for optimal control of nonzero-sum game systems with unknown dynamics. IEEE Trans. Cybern. 46(3), 854–865 (2016)CrossRefGoogle Scholar
  20. 20.
    Yasini, S., Sitani, M.B.N., Kirampor, A.: Reinforcement learning and neural networks for multi-agent nonzero-sum games of nonlinear constrained-input systems. Int. J. Mach. Learn. Cybern. 7(6), 967–980 (2016)CrossRefGoogle Scholar
  21. 21.
    Yasini, S., Karimpour, A., Naghibi Sistani, M.B., Modares, H.: Online concurrent reinforcement learning algorithm to solve twoplayer zerosum games for partially unknown nonlinear continuous time systems. Int. J. Adapt. Control Sig. Process. 29(4), 473–493 (2015)CrossRefzbMATHGoogle Scholar
  22. 22.
    Chowdhary, G., Johnson, E.N., Chandramohan, R., Kimbrell, M.S., Calise, A.: Guidance and control of airplanes under actuator failures and severe structural damage. J. Guid. Control Dyn. 36(4), 1093–1104 (2013)CrossRefGoogle Scholar
  23. 23.
    Tang, Y., Patton, R.J.: Reconfigurable Fault Tolerant Control for nonlinear aircraft based on concurrent SMC-NN adaptor. In: 2014 American Control Conference (ACC), pp. 1267–1272. IEEE (2014)Google Scholar
  24. 24.
    Kannan, S.K., Chowdhary, G.V., Johnson, E.N.: Adaptive control of unmanned aerial vehicles: theory and flight tests. In: Handbook of Unmanned Aerial Vehicles, pp. 613–673. Springer, Dordrecht (2015)Google Scholar
  25. 25.
    Nenchev, D.N., Yoshida, K., Umetani, Y.: Introduction of redundant arms for manipulation in space. In: IEEE International Workshop on Intelligent Robots and Systems, pp. 679–684 (1988)Google Scholar
  26. 26.
    Nenchev, D.N.: Reaction null space of a multibody system with applications in robotics. Mech. Sci. 4(1), 97–112 (2013)CrossRefGoogle Scholar
  27. 27.
    Wang, M., Luo, J., Yuan, J., Walter, U.: Detumbling strategy and coordination control of kinematically redundant space robot after capturing a tumbling target. Nonlinear Dyn. 92(3), 1023–1043 (2018)CrossRefGoogle Scholar
  28. 28.
    Sone, H., Nenchev, D.: Reactionless camera inspection with a free-flying space robot under reaction null-space motion control. Acta Astronautica 128, 707–721 (2016)CrossRefGoogle Scholar
  29. 29.
    Xu, S., Wang, H., Zhang, D., Yang, B.: Adaptive zero reaction motion control for free-floating space manipulators. IEEE Trans. Aerosp. Electron. Syst. 52(3), 1067–1076 (2016)CrossRefGoogle Scholar
  30. 30.
    Chu, Z., Ma, Y., Cui, J.: Adaptive reactionless control strategy via the PSO-ELM algorithm for free-floating space robots during manipulation of unknown objects. Nonlinear Dyn. 91(2), 1321–1335 (2018)CrossRefzbMATHGoogle Scholar
  31. 31.
    Abiko, S., Hirzinger, G.: An adaptive control for a free-floating space robot by using inverted chain approach. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. IROS 2007, pp. 2236–2241. IEEE (2007)Google Scholar
  32. 32.
    Xu, Y., Kanade, T. (eds.): Space Robotics: Dynamics and Control, vol. 188. Springer, Berlin (1992)Google Scholar
  33. 33.
    Haddad, W.M., Chellaboina, V.: Nonlinear Dynamical Systems and Control: A Lyapunov-Based Approach. Princeton University Press, Princeton (2011)zbMATHGoogle Scholar
  34. 34.
    Tao, G.: Adaptive Control Design and Analysis. Wiley, New York (2003)CrossRefzbMATHGoogle Scholar
  35. 35.
    van der Schaft, A.J.: L2-Gain and Passivity Techniques in Nonlinear Control, vol. 2. Springer, London (2000)CrossRefzbMATHGoogle Scholar
  36. 36.
    Chiaverini, S., Siciliano, B.: The unit quaternion: a useful tool for inverse kinematics of robot manipulators. Syst. Anal. Model. Simul. 35(1), 45–60 (1999)zbMATHGoogle Scholar
  37. 37.
    Nenchev, D., Umetani, Y., Yoshida, K.: Analysis of a redundant free-flying spacecraft/manipulator system. IEEE Trans. Robot. Autom. 8(1), 1–6 (1992)CrossRefGoogle Scholar
  38. 38.
    Dorf, R.C., Bishop, R.H.: Modern Control Systems. Pearson (Addison-Wesley), Boston (2011)zbMATHGoogle Scholar
  39. 39.
    Nakamura, Y., Hanafusa, H., Yoshikawa, T.: Task-priority based redundancy control of robot manipulators. Int. J. Robot. Res. 6(2), 3–15 (1987)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Lijun Zong
    • 1
  • Jianjun Luo
    • 1
    Email author
  • Mingming Wang
    • 1
  • Jianping Yuan
    • 1
  1. 1.National Key Laboratory of Aerospace Flight Dynamics, School of AstronauticsNorthwestern Polytechnical UniversityXi’anChina

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