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State Observers for Terrain Mobility Controls: A Technical Analysis

  • Vladimir Vantsevich
  • David Gorsich
  • Andriy Lozynskyy
  • Lyubomyr Demkiv
  • Taras BorovetsEmail author
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)

Abstract

Some sensors in electronic controls may be not available or not feasible for terrain vehicle applications due to severe environmental conditions. The ultimate goal of this paper is to study and demonstrate a possibility to reduce the dependence of terrain mobility controls from sensors. The approach is based on designing reliable observers that can provide robust information for mobility estimation and control systems.

A comparative computational analysis is presented for the following four observers: Extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and Luenberger observer (LO). The observers’ models were integrated in a closed-loop of a real-time feedback controller, which effectiveness was tested in the presence of unknown terrain disturbances and a noise of sensors. The observer-based control system with the state feedback controller is less sensitive to sensor noise than the control system with the sensors that produce states values. EKF, UKF, PF showed more robust results to disturbances than LO. In particular, PF is more robust to sensor noise than the other observers at high levels of noise.

Keywords

State Observers Locomotion Module Terrain Mobility 

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Notes

Acknowledgements

This study has been supported by a grant of the NATO Science for Peace and Security Programme: MYP SPS G5167 “Agile Tyre Mobility for Severe Terrain Environments”.

References

  1. 1.
    Paldan, J. R., Gray, J. P., & Vantsevich, V. V. (2015). Sensor Signal Limitations in Wheel Rotational Kinematics Estimation Model. In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical EngineersGoogle Scholar
  2. 2.
    Boada, B. L., Boada, M. J. L., & Diaz, V. (2016). Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm. Mechanical Systems and Signal Processing, 72, 832-845.Google Scholar
  3. 3.
    Doumiati, M.,Victorino, A., Charara, A.,Lechner, D. (2009).Lateral load transfer and normal forces estimation for vehicle safety: experimental test. Vehicle System Dynamics, 47.Google Scholar
  4. 4.
    Bekker, M.G.(1956).Theory of land locomotion, The University of Michigan Press, Ann Arbor.Google Scholar
  5. 5.
    Vantsevich, V., Gorsich, D., Lozynskyy, A., Demkiv, L., Borovets, T. (2018). State observers: an overview and application to agile tire slippage dynamics // Proceeding of 10th Asia-Pacific Conference of ISTVS. – 2018. – P. 1–18.Google Scholar
  6. 6.
    Gray, J. P., Vantsevich, V. V., Opeiko, A. F., & Hudas, G. R. (2013). A Method for Un-manned Ground Wheeled Vehicle Mobility Estimation in Stochastic Terrain Conditions. In Proc. of the 7th Americas Regional Conference of the ISTVS, Tampa, Florida, USA.Google Scholar
  7. 7.
    Belousov, B., Ksenevich, T. I., Vantsevich, V., & Naumov, S. (2014). An active long-travel, two performance loop control suspension of an open-link locomotion module for off-road applications (No. 2014-01-2288). SAE Technical Paper.Google Scholar
  8. 8.
    Vantsevich, V., Lozynskyy, A., Demkiv, L. (2017)“A Wheel Rotational Velocity Control Strategy for An open-Link Locomotion Module”, Paper # 171, 19th International and 14th European-African Regional Conference of the ISTVS. 25-27, 2017.Google Scholar
  9. 9.
    Gillespie, T. D. (1992). Fundamentals of vehicle dynamics (Vol.114). SAE Technical Paper.Google Scholar
  10. 10.
    Andreev, A. F., Kabanau, V., & Vantsevich, V. (2010). Driveline systems of ground vehicles: theory and design. Crc Press.Google Scholar
  11. 11.
    Wong, J. Y. (2009). Terramechanics and off-road vehicle engineering: terrain behaviour, off-road vehicle performance and design. Butterworth-heinemann.Google Scholar
  12. 12.
    Nuthong, C., (2009). Estimation of Tire-Road Friction Forces using Kalman Filtering for Advanced Vehicle Control (Doctoral dissertation).Google Scholar
  13. 13.
    Gordon, N. J., Salmond, D. J., & Smith, A. F., (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE Proceedings F (Radar and Signal Processing) (Vol. 140, No. 2, pp. 107-113). IET Digital Library.Google Scholar
  14. 14.
    Luenberger, D. (1971). An introduction to observers. IEEE Transactions on automatic control, 16(6), 596-602.Google Scholar
  15. 15.
    Fox, D., Thrun, S., & Burgard, W. (2005). Probabilistic robotics. MIT Press.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vladimir Vantsevich
    • 1
  • David Gorsich
    • 2
  • Andriy Lozynskyy
    • 3
  • Lyubomyr Demkiv
    • 3
  • Taras Borovets
    • 3
    Email author
  1. 1.The University of Alabama at BirminghamBirminghamUSA
  2. 2.US Army RDECOM-TARDECWarrenUSA
  3. 3.National University “Lviv Polytechnic”L’vivs’ka oblastUkraine

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