Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Gaussian Process-Enhanced, External and Internal Convertible (EIC) Form-Based Control of Underactuated Balance Robots (2309.15784v1)

Published 27 Sep 2023 in cs.RO, cs.SY, and eess.SY

Abstract: External and internal convertible (EIC) form-based motion control (i.e., EIC-based control) is one of the effective approaches for underactuated balance robots. By sequentially controller design, trajectory tracking of the actuated subsystem and balance of the unactuated subsystem can be achieved simultaneously. However, with certain conditions, there exists uncontrolled robot motion under the EIC-based control. We first identify these conditions and then propose an enhanced EIC-based control with a Gaussian process data-driven robot dynamic model. Under the new enhanced EIC-based control, the stability and performance of the closed-loop system is guaranteed. We demonstrate the GP-enhanced EIC-based control experimentally using two examples of underactuated balance robots.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (22)
  1. G. Turrisi, M. Capotondi, C. Gaz, V. Modugno, G. Oriolo, and A. D. Luca, “On-line learning for planning and control of underactuated robots with uncertain dynamics,” IEEE Robot. Automat. Lett., vol. 7, no. 1, pp. 358–365, 2022.
  2. N. Kant and R. Mukherjee, “Orbital stabilization of underactuated systems using virtual holonomic constraints and impulse controlled poincaré maps,” Syst. Contr. Lett., vol. 146, pp. 1–9, 2020, article 104813.
  3. F. Han, A. Jelvani, J. Yi, and T. Liu, “Coordinated pose control of mobile manipulation with an unstable bikebot platform,” IEEE/ASME Trans. Mechatronics, vol. 27, no. 6, pp. 4550–4560, 2022.
  4. K. Chen, J. Yi, and D. Song, “Gaussian-process-based control of underactuated balance robots with guaranteed performance,” IEEE Trans. Robotics, vol. 39, no. 1, pp. 572–589, 2023.
  5. F. Han and J. Yi, “Stable learning-based tracking control of underactuated balance robots,” IEEE Robot. Automat. Lett., vol. 6, no. 2, pp. 1543–1550, 2021.
  6. T. Beckers, D. Kulić, and S. Hirche, “Stable Gaussian process based tracking control of Euler–Lagrange systems,” Automatica, vol. 103, pp. 390–397, 2019.
  7. K. Chen and J. Yi, “On the relationship between manifold learning latent dynamics and zero dynamics for human bipedal walking,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., Hamburg, Germany, 2015, pp. 971–976.
  8. J. W. Grizzle, C. Chevallereau, R. W. Sinnet, and A. D. Ames, “Models, feedback control, and open problems in 3D bipedal robotic walking,” Automatica, vol. 50, pp. 1955–1988, 2014.
  9. F. Han, X. Huang, Z. Wang, J. Yi, and T. Liu, “Autonomous bikebot control for crossing obstacles with assistive leg impulsive actuation,” IEEE/ASME Trans. Mechatronics, vol. 27, no. 4, pp. 1882–1890, 2022.
  10. N. Getz, “Dynamic inversion of nonlinear maps with applications to nonlinear control and robotics,” Ph.D. dissertation, Dept. Electr. Eng. and Comp. Sci., Univ. Calif., Berkeley, CA, 1995.
  11. M. Maggiore and L. Consolini, “Virtual holonomic constraints for euler–lagrange systems,” IEEE Trans. Automat. Contr., vol. 58, no. 4, pp. 1001–1008, 2013.
  12. C. C. de Wit, B. Espiau, and C. Urrea, “Orbital stabilization of underactuated mechanical systems,” IFAC Proceedings Volumes, vol. 35, no. 1, pp. 527–532, 2002, 15th IFAC World Congress.
  13. E. R. Westervelt, J. W. Grizzle, and D. E. Koditschek, “Hybrid zero dynamics of planar biped walkers,” IEEE Trans. Automat. Contr., vol. 48, no. 1, pp. 42–56, 2003.
  14. J. W. Grizzle, G. Abba, and F. Plestan, “Asymptotically stable walking for biped robots: Analysis via systems with impulse effects,” IEEE Trans. Automat. Contr., vol. 46, no. 1, pp. 51–64, 2001.
  15. I. Fantoni and R. Lozano and M. W. Spong, “Energy based control of pendubot,” IEEE Trans. Automat. Contr., vol. 45, no. 4, pp. 725–729, 2000.
  16. X. Xin and M. Kaneda, “Analysis of the energy-based control for swinging up two pendulums,” IEEE Trans. Automat. Contr., vol. 50, no. 5, pp. 679–684, 2005.
  17. A. S. Shiriaev, J. W. Perram, and C. Canudas-de-Wit, “Constructive tool for orbital stabilization of underactuated nonlinear systems: Virtual constraints approach,” IEEE Trans. Automat. Contr., vol. 50, no. 8, pp. 1164–1176, 2005.
  18. A. Lederer, Z. Yang, J. Jiao, and S. Hirche, “Cooperative control of uncertain multiagent systems via distributed gaussian processes,” IEEE Trans. Automat. Contr., vol. 68, no. 5, pp. 3091–3098, 2023.
  19. T. Beckers and S. Hirche, “Prediction with approximated gaussian process dynamical models,” IEEE Trans. Automat. Contr., vol. 67, no. 12, pp. 6460–6473, 2022.
  20. M. Deisenroth and J. W. Ng, “Distributed gaussian processes,” in Proc. 32nd Int. Conf. Machine Learning, F. Bach and D. Blei, Eds., vol. 37.   PMLR, 2015, pp. 1481–1490.
  21. M. K. Helwa, A. Heins, and A. P. Schoellig, “Provably robust learning-based approach for high-accuracy tracking control of lagrangian systems,” IEEE Robot. Automat. Lett., vol. 4, no. 2, pp. 1587–1594, 2019.
  22. N. Srinivas, A. Krause, S. M. Kakade, and M. W. Seeger, “Information-theoretic regret bounds for gaussian process optimization in the bandit setting,” IEEE Trans. Inform. Theory, vol. 58, no. 5, pp. 3250–3265, 2012.
Citations (2)

Summary

We haven't generated a summary for this paper yet.