Cascaded Velocity Estimation with Adaptive Complementary Filtering: Implementation on a FIAIWM EGV

Mingcong Cao1, Jinxiang Wang1, Rongrong Wang2, Junmin Wang3, Nan Chen1

  • 1Southeast University
  • 2Shanghai Jiao Tong University
  • 3University of Texas at Austin



Regular Session


10:30 - 12:30 | Mon 19 Aug | Lau, 5-203 | MoA1

Control for Connected and Automated Vehicles

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Based on adaptive complementary filtering (ACF) principles, this paper presents a cascaded estimation method to estimate the longitudinal and lateral velocities of a vehicle. The observation process is first carried out to estimate the longitudinal velocity, followed by the lateral speed observer in another ACF. Both of the ACFs are regulated by a high-pass filter and a low-pass filter with adaptive filtering parameters. Quick-response motor torques are attainable to induce the relevant vehicle states in a dynamic inverse coupled tire model (ICTM). Meanwhile, an additional kinematics-based approach is lumped into ACF to enhance the robustness against modeling discrepancy to zero. The errors of estimation are proved to converge by Lyapunov method. On an electric ground vehicle (EGV) equipped with four independently actuated in-wheel motors (FIAIWM), two maneuvers were conducted to evaluate the proposed method. Experimental results indicate that the proposed estimators are capable of matching with the measured longitudinal and lateral velocities accurately, and highlight it as a low-cost solution in practice.

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