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State estimation applied to non-explicit multibody models

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Abstract

A new state estimation methodology is presented and validated to be applied to complex multibody systems modeled in Adams\({^{\circledR }}\). Traditional state observers are developed to be applied to explicit mathematical models. Given the complexity of some special multibody systems, particularly in vehicle dynamics, simplifications are needed to be able to apply this control technique. Nevertheless, tridimensional multibody systems are usually completely modeled in multibody dynamics simulation software, such as Adams\({^{\circledR }}\) from MSC Software, for simulation, analysis and optimization, including highly nonlinear dynamic elements, such as the tires and the silent blocks in vehicle dynamics. Therefore, applying state observers to these non-explicit models is a real need in order to continue improving control systems on these systems. The methodology being presented and validated is a first step in this direction.

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References

  1. Hamelin, P., Bigras, P., Beaudry, J., Richard, P.-L., Blain, M.: Discrete-time state feedback with velocity estimation using a dual observer: application to an underwater direct-drive grinding robot. IEEE/ASME Trans. Mechatron. 17, 187–191 (2012)

    Article  Google Scholar 

  2. Yang, Y., Balakrishnan, S.N., Tang, L., Landers, R.G.: Electrohydraulic control using neural MRAC based on a modified state observer. IEEE/ASME Trans. Mechatron. 18, 867–877 (2013)

    Article  Google Scholar 

  3. Davis, T.A., Shin, Y.C., Yao, B.: Observer-based adaptive robust control of friction stir welding axial force. IEEE/ASME Trans. Mechatron. 16, 1032–1039 (2011)

    Article  Google Scholar 

  4. Lakshmanan, S., Park, J.H., Rakkiyappan, R., Jung, H.Y.: State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach. J. Nonlinear Dyn. 46, 99–108 (2013)

    MathSciNet  MATH  Google Scholar 

  5. Huang, Y., Cheng, P., Menq, C.-H.: Dynamic force sensing using an optically trapped probing system. IEEE/ASME Trans. Mechatron. 16, 1145–1154 (2011)

    Article  Google Scholar 

  6. Berkhizir, N., Phan, M.Q., Betti, R., Longman, R.: Identification of discrete-time bilinear systems through equivalent linear models. Nonlinear Dyn. 69, 2065–2078 (2012)

    Article  MathSciNet  Google Scholar 

  7. Luque, P., Mántaras, D.A., Fidalgo, E., Álvarez, J., Riva, P., Girón, P., Compadre, D., Ferran, J.: Tyre-road grip coefficient assessment—Part II: online estimation using instrumented vehicle, extended Kalman filter, and neural network. Veh. Syst. Dyn. 51, 1872–1893 (2013)

    Article  Google Scholar 

  8. Hsu, L.-Y., Chen, T.-L.: Vehicle full-state estimation and prediction system using state observers. IEEE/ASME Trans. Mechatron. 58, 2651–2662 (2009)

    Google Scholar 

  9. Baffet, G., Charara, A., Dherbomez, G.: An observer of tire-road forces and friction for active security vehicle systems. IEEE/ASME Trans. Mechatron. 12, 651–661 (2007)

    Article  Google Scholar 

  10. Doumiati, M., Victorino, A.C., Charara, A., Lechner, D.: Onboard real-time estimation of vehicle lateral tire-road forces and sideslip angle. IEEE/ASME Trans. Mechatron. 16, 601–614 (2011)

    Article  Google Scholar 

  11. Davoodabadi, I., Ramezani, A., Mahmoodi-k, M., Ahmadizadeh, P.: Identification of tire forces using dual unscented Kalman filter algorithm. Nonlinear Dyn. 78, 1907–1919 (2014)

    Article  Google Scholar 

  12. Kurode, S., Spurgeon, S.K., Bandyopadhyay, B., Gandhi, P.S.: Sliding mode control for slosh-free motion using a nonlinear sliding surface. IEEE/ASME Trans. Mechatron. 18, 714–724 (2013)

    Article  Google Scholar 

  13. Domínguez, J.R., Mora-Soto, C., Ortega-Cisneros, S., Panduro, J.J.R., Loukianov, A.G.: Copper and core loss minimization for induction motors using high-order sliding-mode control. IEEE/ASME Trans. Ind. Electron. 59, 2877–2889 (2012)

    Article  Google Scholar 

  14. Ibaraki, S., Suryanarayanan, S., Tomizuka, M.: Design of Luenberger state observers using fixed-structure H\(\infty \) optimization and its application to fault detection in lane-keeping control of automated vehicles. IEEE/ASME Trans. Mechatron. 10, 34–42 (2005)

    Article  Google Scholar 

  15. Satkoski, C., Shaver, G.: Piezoelectric fuel injection: pulse-to-pulse coupling and flow rate estimation. IEEE/ASME Trans. Mechatron. 16, 627–642 (2011)

    Article  Google Scholar 

  16. Lin, Y.-W., Cheng, J.-W.J.: A high-gain observer for a class of cascade-feedback-connected nonlinear systems with application to injection molding. IEEE/ASME Trans. Mechatron. 15, 714–727 (2010)

    Article  Google Scholar 

  17. Gulati, N., Barth, E.J.: A globally stable, load-independent pressure observer for the servo control of pneumatic actuators. IEEE/ASME Trans. Mechatron. 14, 295–306 (2009)

    Article  Google Scholar 

  18. Leavitt, J., Sideris, A., Bobrow, J.E.: High bandwidth tilt measurement using low-cost sensors. IEEE/ASME Trans. Mechatron. 11, 320–327 (2006)

    Article  Google Scholar 

  19. Kuo, S.-K., Menq, C.-H.: Modeling and control of a six-axis precision motion control stage. IEEE/ASME Trans. Mechatron. 10, 50–59 (2005)

    Article  Google Scholar 

  20. Yue, X., Vilathgamuwa, D.M., Tseng, K.-J.: Robust adaptive control of a three-axis motion simulator with state observers. IEEE/ASME Trans. Mechatron. 10, 50–59 (2005)

    Article  Google Scholar 

  21. de Jalón, J.G., Bayo, E.: Kinematic and dynamic of multibody systems—the real-time challenge. Springer, New York (1994)

    Book  Google Scholar 

  22. Huang, J., Tomizuka, M.: LTV controller design for vehicle lateral control under fault in rear sensors. IEEE/ASME Trans. Mechatron. 10, 1–7 (2005)

    Article  Google Scholar 

  23. Mántaras, D.A., Luque, P.: Virtual test rig to improve the design and optimization process of the vehicle steering and suspension systems. Veh. Syst. Dyn. 50, 1563–1584 (2012)

    Article  Google Scholar 

  24. Jazar, R.N.: Vehicle Dynamics: Theory and applications. Springer, New York (2008)

    Book  Google Scholar 

  25. Rajamani, R.: Vehicle Dynamics and Control. Springer, New York (2006)

    MATH  Google Scholar 

  26. Kiencke, U., Nielsen, L.: Automotive Control Systems: For Engine, Driveline and Vehicle. Springer, New York (2005)

    Book  Google Scholar 

  27. Blundell, M., Harty, D.: The Multibody Systems Approach to Vehicle Dynamics. SAE International, Warrendale (2004)

    Google Scholar 

  28. Pacejka, H.B.: Tyre and Vehicle Dynamics. Butterworth-Heinemann, Oxford (2002)

    MATH  Google Scholar 

  29. Genta, G.: Motor Vehicle Dynamics: Modelling and Simulation. World Scientific, Singapore (1997)

    Book  MATH  Google Scholar 

  30. Milliken, W.F., Milliken, D.L.: Race Car Vehicle Dynamics. Society of Automotive Engineers Inc., Warrendale (1995)

    Google Scholar 

  31. Ammon, D.: Vehicle dynamics analysis tasks and related tyre simulation challenges. Veh. Syst. Dyn. 43, 30–47 (2005)

    Article  Google Scholar 

  32. Chunli, Y., Jianguo, Y.: Transient and steady-state handling characteristics of forest fire patrolling vehicle based on ADAMS\(\textregistered \). in Sec. Int. Conf. Rec. on Multimedia and Information Technology, pp. 210–212 (2010)

  33. http://iftomm-multibody.org/software

  34. http://www.simpack.com

  35. http://www.mscsoftware.com

  36. http://www.maplesoft.com/dynaflexpro/

  37. http://www.itm.uni-stuttgart.de/research/neweul/

  38. http://www.samcef.com

  39. http://eng.functionbay.co.kr

  40. http://www.ifm-chemnitz.de

  41. http://www.prm.ucl.ac.be/robotran

  42. http://www.wa.ctw.utwente.nl/Software/SPACAR/

  43. http://www.roboanalyzer.com/about.html

  44. http://www.hotint.org

  45. http://www.redysim.co.nr

  46. http://www.mbdyn.org

  47. http://mat21.etsii.upm.es/mbs/mbs3d/

  48. Mántaras, D.A., Luque, P., Nava, J.A., Riva, P., Girón, P., Compadre, D., Ferran, J.: Tyre-road grip coefficient assessment. Part 1: off-line methodology using multibody dynamic simulation and genetic algorithms. Veh. Syst. Dyn. 51, 1603–1618 (2013)

    Article  Google Scholar 

  49. Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)

    Article  Google Scholar 

  50. Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE. 92, 401–422 (2004)

    Article  Google Scholar 

  51. Venture, G., Ripert, P.-J., Khalil, W., Gautier, M., Bodson, P.: Modeling and identification of passenger car dynamics using robotics formalism. IEEE Trans. Intell. Transp. Syst. 7, 349–359 (2006)

    Article  Google Scholar 

  52. Mántaras, D.A., Luque, P., Vera, C.: Development and validation of a 3-dimensional kinematic model for the McPherson steering and suspension mechanisms. J. Mech. Mach. Theory 39, 603–619 (2004)

    Article  MATH  Google Scholar 

  53. Maybeck, P.S.: Stochastic Models, Estimation, and Control. Academic Press P. S. Inc. 1, London (1979)

    MATH  Google Scholar 

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Acknowledgments

The authors would like to thank the company Centro Técnico SEAT S.L., who generously provided the vehicle and the Adams\(^\circledR \) model.

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Authors and Affiliations

  1. Transportation Department, University of Oviedo, 33203, Gijón, Asturias, Spain

    Carlos Cuesta, Pablo Luque & Daniel A. Mántaras

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  1. Carlos Cuesta
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  2. Pablo Luque
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  3. Daniel A. Mántaras
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Correspondence to Pablo Luque.

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Cuesta, C., Luque, P. & Mántaras, D.A. State estimation applied to non-explicit multibody models. Nonlinear Dyn 86, 1673–1686 (2016). https://doi.org/10.1007/s11071-016-2985-9

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  • DOI: https://doi.org/10.1007/s11071-016-2985-9

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