Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 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

LMI-based robust model predictive control for a quarter car with series active variable geometry suspension (2401.06650v2)

Published 12 Jan 2024 in eess.SY and cs.SY

Abstract: This paper proposes a robust model predictive control-based solution for the recently introduced series active variable geometry suspension (SAVGS) to improve the ride comfort and road holding of a quarter car. In order to close the gap between the nonlinear multi-body SAVGS model and its linear equivalent, a new uncertain system characterization is proposed that captures unmodeled dynamics, parameter variation, and external disturbances. Based on the newly proposed linear uncertain model for the quarter car SAVGS system, a constrained optimal control problem (OCP) is presented in the form of a linear matrix inequality (LMI) optimization. More specifically, utilizing semidefinite relaxation techniques a state-feedback robust model predictive control (RMPC) scheme is presented and integrated with the nonlinear multi-body SAVGS model, where state-feedback gain and control perturbation are computed online to optimise performance, while physical and design constraints are preserved. Numerical simulation results with different ISO-defined road events demonstrate the robustness and significant performance improvement in terms of ride comfort and road holding of the proposed approach, as compared to the conventional passive suspension, as well as, to actively controlled SAVGS by a previously developed conventional H-infinity control scheme.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. C. Arana, S. A. Evangelou, and D. Dini, “Series active variable geometry suspension application to chassis attitude control,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 1, pp. 518–530, 2016.
  2. ——, “Series active variable geometry suspension application to comfort enhancement,” Control Engineering Practice, vol. 59, pp. 111–126, 2017.
  3. M. Yu, C. Arana, S. A. Evangelou, and D. Dini, “Quarter-car experimental study for series active variable geometry suspension,” IEEE Transactions on Control Systems Technology, vol. 27, no. 2, pp. 743–759, 2017.
  4. C. Arana, “Active variable geometry suspension for cars,” Ph.D. dissertation, Imperial College London, 2015.
  5. C. Cheng and S. A. Evangelou, “Series active variable geometry suspension robust control based on full-vehicle dynamics,” Journal of Dynamic Systems, Measurement, and Control, vol. 141, no. 5, p. 051002, 2019.
  6. M. Yu, C. Cheng, S. A. Evangelou, and D. Dini, “Robust control for a full-car prototype of series active variable geometry suspension,” in 2019 IEEE 58th Conference on Decision and Control (CDC).   IEEE, 2019, pp. 7615–7622.
  7. ——, “Series active variable geometry suspension: Full-car prototyping and road testing,” IEEE/ASME Transactions on Mechatronics, 2021.
  8. Z. Feng, M. Yu, C. Cheng, S. A. Evangelou, I. M. Jaimoukha, and D. Dini, “Uncertainties investigation and μ𝜇\muitalic_μ-synthesis control design for a full car with series active variable geometry suspension,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 13 882–13 889, 2020.
  9. Z. Feng, M. Yu, S. A. Evangelou, I. M. Jaimoukha, and D. Dini, “Mu-synthesis pid control of full-car with parallel active link suspension under variable payload,” IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 176–189, 2023.
  10. D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O.M. Scokaert, “Constrained model predictive control: Stability and optimality,” Automatica, vol. 36, no. 6, pp. 789–814, 2000.
  11. S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, pp. 733–764, 2003.
  12. Mehra, Raman K and Amin, Jayesh N and Hedrick, Karl J and Osorio, Carlos and Gopalasamy, Srinivasan, “Active suspension using preview information and model predictive control,” in Proceedings of the 1997 IEEE international conference on control applications.   IEEE, 1997, pp. 860–865.
  13. C. Göhrle, A. Schindler, A. Wagner, and O. Sawodny, “Model predictive control of semi-active and active suspension systems with available road preview,” in 2013 European Control Conference (ECC).   IEEE, 2013, pp. 1499–1504.
  14. J. Yao, M. Wang, Z. Li, and Y. Jia, “Research on model predictive control for automobile active tilt based on active suspension,” Energies, vol. 14, no. 3, 2021. [Online]. Available: https://www.mdpi.com/1996-1073/14/3/671
  15. E. Enders, G. Burkhard, and N. Munzinger, “Analysis of the influence of suspension actuator limitations on ride comfort in passenger cars using model predictive control,” Actuators, vol. 9, no. 3, 2020. [Online]. Available: https://www.mdpi.com/2076-0825/9/3/77
  16. J. Theunissen, A. Sorniotti, P. Gruber, S. Fallah, M. Ricco, M. Kvasnica, and M. Dhaens, “Regionless explicit model predictive control of active suspension systems with preview,” IEEE Transactions on Industrial Electronics, vol. 67, no. 6, pp. 4877–4888, 2020.
  17. D. Rodriguez-Guevara, A. Favela-Contreras, F. Beltran-Carbajal, C. Sotelo, and D. Sotelo, “A differential flatness-based model predictive control strategy for a nonlinear quarter-car active suspension system,” Mathematics, vol. 11, no. 4, 2023. [Online]. Available: https://www.mdpi.com/2227-7390/11/4/1067
  18. P. O.M. Scokaert and D. Q. Mayne, “Min-max feedback model predictive control for constrained linear system,” IEEE Transaction on Automatic Control, no. 43, pp. 1136–1142, 1998.
  19. E. Kerrigan and J.M. Maciejowski, “Feedback min–max model predictive control using a single linear program: robust stability and the explicit solution,” International Journal of Robust and Nonlinear Control, vol. 14, pp. 395 – 413, 2004.
  20. M. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrained model predictive control using linear matrix inequalities,” Automatica, vol. 32, no. 10, pp. 1361–1379, 1996.
  21. F. A. Cuzzola, J. C. Geromel, and M. Morari, “An improved approach for constrained robust model predictive control,” Automatica, vol. 38, no. 7, pp. 1183–1189, 2002.
  22. G. Emanuele and C. Alessandro, “Receding horizon control strategies for constrained lpv systems based on a class of nonlinearly parameterized lyapunov functions,” IEEE Transactions on Automatic Control, vol. 57, no. 9, pp. 2354–2360, 2012.
  23. F. Tahir and I. M. Jaimoukha, “Causal state-feedback parameterizations in robust model predictive control,” Automatica, vol. 49, no. 9, pp. 2675–2682, 2013.
  24. J. Fleming, B. Kouvaritakis, and M. Cannon, “Robust tube mpc for linear systems with multiplicative uncertainty,” IEEE Transactions on Automatic Control, vol. 60, no. 4, pp. 1087–1092, 2014.
  25. J. Hanema, M. Lazar, and R. Tóth, “Heterogeneously parameterized tube model predictive control for lpv systems,” Automatica, vol. 111, p. 108622, 2020.
  26. M. Kang, R. Chen, and Y. Li, “Adaptive tube-based model predictive control for vehicle active suspension system,” in 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI).   IEEE, 2020, pp. 720–725.
  27. W. Langson, I. Chryssochoos, S. V. Raković, and D.Q. Mayne, “Robust model predictive control using tubes,” Automatica, vol. 40, no. 1, pp. 125 – 133, 2004.
  28. D. Q. Mayne, M. Seron, and J. D. Dona, “Robust model predictive control of constrained linear systems with bounded disturbances,” Automatica, vol. 41, no. 2, pp. 219–224, 2005.
  29. M. Bujarbaruah, U. Rosolia, Y. R. Stürz, and F. Borrelli, “A simple robust mpc for linear systems with parametric and additive uncertainty,” in 2021 American Control Conference (ACC).   IEEE, 2021, pp. 2108–2113.
  30. W. Vilaivannaporn, S. Boonsith, W. Pornputtapitak, and P. Bumroongsri, “Robust output feedback predictive controller with adaptive invariant tubes and observer gains,” International Journal of Dynamics and Control, vol. 9, no. 2, pp. 755–765, 2021.
  31. A. Georgiou, F. Tahir, I. M. Jaimoukha, and S. A. Evangelou, “Computationally efficient robust model predictive control for uncertain system using causal state-feedback parameterization,” IEEE Transactions on Automatic Control, pp. 1–8, 2022.
  32. A. Georgiou, “Robust model predictive control for linear systems subject to norm-bounded model uncertainties and disturbances: An implementation to industrial directional drilling system,” Ph.D. dissertation, Imperial College London, 2022.
  33. M. Sayers, “Autosim,” Vehicle System Dynamics, vol. 22, no. S1, pp. 53–56, 1993.
  34. C. Mousseau, M. W. Sayers, and D. Fagan, “Symbolic quasi-static and dynamic analyses of complex automobile models,” Vehicle System Dynamics, vol. 20, no. sup1, pp. 446–459, 1992.
  35. R. Sharp and S. Hassan, “An evaluation of passive automotive suspension systems with variable stiffness and damping parameters,” Vehicle System Dynamics, vol. 15, no. 6, pp. 335–350, 1986.
  36. H. E. Vongierke, “The iso standard: Guide for the evaluation of human exposure to whole-body vibration,” in NASA. Langley Res. Center The 1975 Ride Quality Symp., 1975.
  37. R. Sharp and D. Crolla, “Road vehicle suspension system design-a review,” Vehicle system dynamics, vol. 16, no. 3, pp. 167–192, 1987.
  38. J. Skaf and . S. P. Boyd, “Design of affine controllers via convex optimization,” IEEE Transactions on Automatic Control, vol. 55, pp. 2476–2487, 2010.
  39. F. Tahir and I. Jaimoukha, “Causal state-feedback parameterization in robust model predictive control,” Automatica, vol. 49, no. 9, pp. 2675–2682, 2013.
  40. ISO, “8608:2016, Mechanical vibration - Road surface profiles - reporting of measured data,” Geneva, Switzerland, 2016.
  41. M. Yu, S. A. Evangelou, and D. Dini, “Model identification and control for a quarter car test rig of series active variable geometry suspension,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 3376–3381, 2017.

Summary

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

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