Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
166 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 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

Robustness-Guaranteed Observer-Based Control Strategy with Modularity for Cleantech EMLA-Driven Heavy-Duty Robotic Manipulator (2311.15843v4)

Published 27 Nov 2023 in eess.SY and cs.SY

Abstract: This paper introduces an innovative observer-based modular control strategy in a class of n_a-degree-of-freedom (DoF) fully electrified heavy-duty robotic manipulators (HDRMs) to (1) guarantee robustness in the presence of uncertainties and disturbances, (2) address the complexities arising from several interacting mechanisms, (3) ensure uniformly exponential stability, and (4) enhance overall control performance. To begin, the dynamic model of HDRM actuation systems, which exploits the synergy between cleantech electromechanical linear actuators (EMLAs) and permanent magnet synchronous motors (PMSMs), is investigated. In addition, the reference trajectories of each joint are computed based on direct collocation with B-spline curves to extract the key kinematic and dynamic quantities of HDRMs. To guarantee robust tracking of the computed trajectories by the actual motion states, a novel control methodology, called robust subsystem-based adaptive (RSBA) control, is enhanced through an adaptive state observer. The RSBA control addresses inaccuracies inherent in motion, including modeling errors, non-triangular uncertainties, and both torque and voltage disturbances, to which the EMLA-driven HDRM is susceptible. Furthermore, this approach is presented in a unified generic equation format for all subsystems to mitigate the complexities of the overall control system. By applying the RSBA architecture, the uniformly exponential stability of the EMLA-driven HDRM is proven based on the Lyapunov stability theory. The proposed RSBA control performance is validated through simulations and experiments of the scrutinized PMSM-powered EMLA-actuated mechanisms.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. P. Agreement, “Paris agreement,” in report of the conference of the parties to the United Nations framework convention on climate change (21st session, 2015: Paris). Retrived December, vol. 4.   HeinOnline, 2015, p. 2017.
  2. R. Bischoff and T. Guhl, “The strategic research agenda for robotics in europe [industrial activities],” IEEE Robotics & Automation Magazine, vol. 17, no. 1, pp. 15–16, 2010.
  3. M. Daily, S. Medasani, R. Behringer, and M. Trivedi, “Self-driving cars,” Computer, vol. 50, no. 12, pp. 18–23, 2017.
  4. C. Badue, R. Guidolini, R. V. Carneiro, P. Azevedo, V. B. Cardoso, A. Forechi, L. Jesus, R. Berriel, T. M. Paixao, F. Mutz et al., “Self-driving cars: A survey,” Expert Systems with Applications, vol. 165, p. 113816, 2021.
  5. W. Cao, B. C. Mecrow, G. J. Atkinson, J. W. Bennett, and D. J. Atkinson, “Overview of electric motor technologies used for more electric aircraft (mea),” IEEE Transactions on Industrial Electronics, vol. 59, no. 9, pp. 3523–3531, 2011.
  6. J. Li, Z. Yu, Y. Huang, and Z. Li, “A review of electromechanical actuation system for more electric aircraft,” in 2016 IEEE International Conference on Aircraft Utility Systems (AUS).   2016 IEEE International Conference on Aircraft Utility Systems (AUS), 2016, pp. 490–497.
  7. M. Habibnejad Korayem, N. Ghobadi, and S. Fathollahi Dehkordi, “Designing an optimal control strategy for a mobile manipulator and its application by considering the effect of uncertainties and wheel slipping,” Optimal Control Applications and Methods, vol. 42, no. 5, pp. 1487–1511, 2021.
  8. M. Bahari, A. Paz, A. S. Habib, and J. Mattila, “Performance evaluation of an electromechanical linear actuator with optimal trajectories,” in 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023, pp. 1–7.
  9. K. L. Fleming, A. L. Brown, L. Fulton, and M. Miller, “Electrification of medium-and heavy-duty ground transportation: Status report,” Current Sustainable/Renewable Energy Reports, vol. 8, no. 3, pp. 180–188, 2021.
  10. I. Boldea and S. A. Nasar, “Linear electric actuators and generators,” IEEE Transactions on Energy Conversion, vol. 14, no. 3, pp. 712–717, 1999.
  11. C. Knabe, B. Lee, V. Orekhov, and D. Hong, “Design of a compact, lightweight, electromechanical linear series elastic actuator,” in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.   ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2014, p. V05BT08A014.
  12. A. Redekar, D. Deb, and S. Ozana, “Functionality analysis of electric actuators in renewable energy systems—a review,” Sensors, vol. 22, no. 11, p. 4273, 2022.
  13. Väisänen, T., “(2023). Simulation of electro-mechanically actuated boom,” Master’s thesis, Tampere University.
  14. N. Nagel, “Actuation challenges in the more electric aircraft: Overcoming hurdles in the electrification of actuation systems,” IEEE Electrification Magazine, vol. 5, no. 4, pp. 38–45, 2017.
  15. B. Lequesne, “Automotive electrification: The nonhybrid story,” IEEE Transactions on Transportation Electrification, vol. 1, no. 1, pp. 40–53, 2015.
  16. W. Hassan and B. Wang, “Efficiency optimization of pmsm based drive system,” in Proceedings of The 7th International Power Electronics and Motion Control Conference.   IEEE 7th International Power Electronics and Motion Control Conference, 2012, pp. 1027–1033.
  17. L.-B. Li, H.-X. Sun, J.-D. Chu, and G.-L. Wang, “The predictive control of pmsm based on state space.”   IEEE Proceedings of the 2003 International Conference on Machine Learning and Cybernetics, 2003, pp. 859–862.
  18. A. Hagras, “Nonlinear adaptive extended state space predictive control of permanent magnet synchronous motor,” International Transactions on Electrical Energy Systems, vol. 29, no. 1, p. e2677, 2019.
  19. Y. X. Su, C. H. Zheng, and B. Y. Duan, “Automatic disturbances rejection controller for precise motion control of permanent-magnet synchronous motors,” IEEE Transactions on Industrial Electronics, vol. 52, no. 3, pp. 814–823, 2005.
  20. Z. Ma and S. Tong, “Nonlinear filters-based adaptive fuzzy control of strict-feedback nonlinear systems with unknown asymmetric dead-zone output,” IEEE Transactions on Automation Science and Engineering, 2023.
  21. M. Saeedi, J. Zarei, M. Saif, D. Shanahan, and A. Montazeri, “Resilient event-triggered terminal sliding mode control design for a robot manipulator,” IEEE Transactions on Automation Science and Engineering, 2023.
  22. R. Hao, J. Wang, J. Zhao, and S. Wang, “Observer-based robust control of 6-dof parallel electrical manipulator with fast friction estimation,” IEEE Transactions on Automation Science and Engineering, vol. 13, no. 3, pp. 1399–1408, 2015.
  23. J. Liang, Y. Chen, Y. Wu, Z. Miao, H. Zhang, and Y. Wang, “Adaptive prescribed performance control of unmanned aerial manipulator with disturbances,” IEEE Transactions on Automation Science and Engineering, 2022.
  24. S. K. Pradhan and B. Subudhi, “Real-time adaptive control of a flexible manipulator using reinforcement learning,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 2, pp. 237–249, 2012.
  25. B. Calli and A. M. Dollar, “Robust precision manipulation with simple process models using visual servoing techniques with disturbance rejection,” IEEE Transactions on Automation Science and Engineering, vol. 16, no. 1, pp. 406–419, 2018.
  26. M. Lyu, G. Wu, D. Luo, F. Rong, and S. Huang, “Robust nonlinear predictive current control techniques for pmsm,” Energies, vol. 12, no. 3, p. 443, 2019.
  27. T. Türker, U. Buyukkeles, and A. F. Bakan, “A robust predictive current controller for pmsm drives,” IEEE Transactions on Industrial Electronics, vol. 63, no. 6, pp. 3906–3914, 2016.
  28. Y. Kim, H.-T. Seo, S.-K. Kim, and K.-S. Kim, “A robust current controller for uncertain permanent magnet synchronous motors with a performance recovery property for electric power steering applications,” Energies, vol. 11, no. 5, p. 1224, 2018.
  29. F. Zhou, J. He, M. Zhang, Y. Xiao, Z. Chen, T.-W. Wong, T. Li, Z. Xu, and Y. Luo, “Electromechanical model-based adaptive control of multilayered dielectric elastomer bending actuator,” Journal of Applied Mechanics, vol. 88, no. 11, p. 111006, 2021.
  30. X. Sun, H. Yu, J. Yu, and X. Liu, “Design and implementation of a novel adaptive backstepping control scheme for a pmsm with unknown load torque,” IET Electric Power Applications, vol. 13, no. 4, pp. 445–455, 2019.
  31. H.-W. Kim, S.-M. Park, S.-J. Kim, and J. Y. Choi, “Adaptive backstepping speed control for pmsm with mechanical parametric uncertainties,” in 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE).   IEEE, 2016, pp. 427–430.
  32. X. Wang, W. Wang, L. Li, J. Shi, and B. Xie, “Adaptive control of dc motor servo system with application to vehicle active steering,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 3, pp. 1054–1063, 2019.
  33. W. Wang, B. Xie, Z. Zuo, and H. Fan, “Adaptive backstepping control of uncertain gear transmission servosystems with asymmetric dead-zone nonlinearity,” IEEE Transactions on Industrial Electronics, vol. 66, no. 5, pp. 3752–3762, 2018.
  34. W. Zhang, Z. Ping, Y. Fu, S. Zheng, and P. Zhang, “Observer-based backstepping adaptive force control of electro-mechanical actuator with improved lugre friction model,” Aerospace, vol. 9, no. 8, p. 415, 2022.
  35. M. S. Rafaq, A. T. Nguyen, H. H. Choi, and J.-W. Jung, “A robust high-order disturbance observer design for sdre-based suboptimal speed controller of interior pmsm drives,” IEEE Access, vol. 7, pp. 165 671–165 683, 2019.
  36. L. Xiaoquan, L. Heyun, and H. Junlin, “Load disturbance observer-based control method for sensorless pmsm drive,” IET Electric Power Applications, vol. 10, no. 8, pp. 735–743, 2016.
  37. M. Zhang, M. Zhou, H. Liu, B. Zhang, Y. Zhang, and H. Chu, “Friction compensation and observer-based adaptive sliding mode control of electromechanical actuator,” Advances in Mechanical Engineering, vol. 10, no. 12, p. 1687814018813793, 2018.
  38. J. Yu, P. Shi, W. Dong, B. Chen, and C. Lin, “Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors,” IEEE transactions on neural networks and learning systems, vol. 26, no. 3, pp. 640–645, 2014.
  39. L. Liu, W. Zhao, Y.-J. Liu, S. Tong, and Y.-Y. Wang, “Adaptive finite-time neural network control of nonlinear systems with multiple objective constraints and application to electromechanical system,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5416–5426, 2020.
  40. M. Bahari, F. Tootoonchian, and A. Mahmoudi, “An electromagnetic design of slotless variable reluctance pm-resolver,” IEEE Transactions on Industrial Electronics, vol. 70, no. 5, pp. 5336–5346, 2023.
  41. D. Shin, W. Kim, and C. C. Chung, “Position control of a permanent magnet stepper motor by miso backstepping in semi-strict feedback form,” in 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).   IEEE, 2011, pp. 808–813.
  42. Xing, L. and Wen, C., “(2023). Dynamic event-triggered adaptive control for a class of uncertain nonlinear systems,” Automatica,, vol. 158, p. 111286.
  43. Jiang, Y., Fan, J., Gao, W., Chai, T., and Lewis, F. L., “(2020). Cooperative adaptive optimal output regulation of nonlinear discrete-time multi-agent systems,” Automatica,, vol. 121, p. 109149.
  44. Wang, J., Li, J., He, C., and Liu, S., “(2021). Output regulation for the parametric strict-feedback system via the event-triggered adaptive updating algorithm,” Applied Mathematical Modelling,, vol. 96, pp. 598–616.
  45. Bernard, P., Bin, M., and Marconi, L., “(2020). Adaptive output regulation via nonlinear luenberger observer-based internal models and continuous-time identifiers,” Automatica,, vol. 122, p. 109261.
  46. Koivumäki, J., Humaloja, J. P., Paunonen, L., Zhu, W. H., and Mattila, J., “(2022). Subsystem-based control with modularity for strict-feedback form nonlinear systems,” IEEE Transactions on Automatic Control.
  47. J. Cai, C. Wen, H. Su, Z. Liu, and L. Xing, “Adaptive backstepping control for a class of nonlinear systems with non-triangular structural uncertainties,” IEEE Transactions on Automatic Control, vol. 62, no. 10, pp. 5220–5226, 2016.
  48. Cai, J., Wen, C., Xing, L., and Yan, Q., “(2020). Decentralized backstepping control for interconnected systems with non-triangular structural uncertainties,” IEEE Transactions on Automatic Control,, vol. 68, no. 3, pp. 1692–1699.
  49. M. Heydari Shahna, M. Bahari, and J. Mattila, “Robust decomposed system control for an electro-mechanical linear actuator mechanism under input constraints,” International Journal of Robust and Nonlinear Control, 2024.
  50. S. Li and Z. Liu, “Adaptive speed control for permanent-magnet synchronous motor system with variations of load inertia,” IEEE transactions on industrial electronics, vol. 56, no. 8, pp. 3050–3059, 2009.
  51. J. Zhang, W. Ren, J. Li, and X.-M. Sun, “Adaptive neural asymptotic tracking control for pmsm systems under current constraints and unknown dynamics,” IEEE Transactions on Circuits and Systems II: Express Briefs, 2023.
  52. J. Yang, W.-H. Chen, S. Li, L. Guo, and Y. Yan, “Disturbance/uncertainty estimation and attenuation techniques in pmsm drives—a survey,” IEEE Transactions on Industrial Electronics, vol. 64, no. 4, pp. 3273–3285, 2016.
  53. M. De Soricellis, D. Da Ru, and S. Bolognani, “A robust current control based on proportional-integral observers for permanent magnet synchronous machines,” IEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1437–1447, 2017.
  54. J. Liu, Q.-G. Wang, and J. Yu, “Command-filter-approximator-based adaptive control for uncertain nonlinear systems and its application in pmsms,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023.
  55. S.-H. Lee, J. Kim, F. C. Park, M. Kim, and J. E. Bobrow, “Newton-type algorithms for dynamics-based robot movement optimization,” IEEE Transactions on robotics, vol. 21, no. 4, pp. 657–667, 2005.
  56. A. Paz and G. Arechavaleta, “Practical guide to solve the minimum-effort problem with geometric algorithms and b-splines,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 6720–6726.
  57. Petrović, G. R. and Mattila, J., “(2022). Mathematical modelling and virtual decomposition control of heavy-duty parallel–serial hydraulic manipulators,” Mechanism and Machine Theory,, vol. 170, p. 104680.
  58. Chen, Y., “(1990). Adaptive robust observers for non-linear uncertain systems,” International Journal of Systems Science,, vol. 21, no. 5, pp. 803–814.
  59. M. H. Shahna and J. Mattila, “Exponential auto-tuning fault-tolerant control of n degrees-of-freedom manipulators subject to torque constraints,” arXiv preprint arXiv:2311.15852, 2023.
  60. M. Corless and G. Leitmann, “Bounded controllers for robust exponential convergence,” Journal of Optimization Theory and Applications, vol. 76, no. 1, pp. 1–12, 1993.

Summary

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

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now