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Calculation Algorithm of Tire-Road Friction Coefficient Based on Limited-Memory Adaptive Extended Kalman Filter

Author

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  • Bin Huang
  • Xiang Fu
  • Sen Wu
  • Song Huang

Abstract

In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

Suggested Citation

  • Bin Huang & Xiang Fu & Sen Wu & Song Huang, 2019. "Calculation Algorithm of Tire-Road Friction Coefficient Based on Limited-Memory Adaptive Extended Kalman Filter," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-14, May.
    • Handle: RePEc:hin:jnlmpe:1056269
    DOI: 10.1155/2019/1056269
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    Cited by:

    1. Ruolan Fan & Gang Li & Yanan Wu, 2023. "State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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