Robust estimation of tire road peak adhesion coefficient based on excitation assessment

The robust and accurate estimation of the tire-road peak adhesion coefficient (TRPAC) serves not only as a critical input for active safety control systems but also as an essential parameter in intelligent chassis dynamics control systems. To enhance the robustness of the TRPAC estimation algorithm while maintaining accuracy, this paper proposes a robust estimation algorithm based on excitation assessment. First, a TRPAC estimator integrating lateral and longitudinal excitations is established. The lateral excitation component of the estimator employs a nonlinear observer and the aligning moment analysis for TRPAC estimation, incorporating adaptive gain coefficients and excitation-dependent update criteria strategies. The longitudinal excitation component utilizes <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>φ</mml:mi> <mml:mo>−</mml:mo> <mml:mi>λ</mml:mi> </mml:mrow> </mml:math> curve analysis for TRPAC estimation. Second, based on aligning moment curves of tires and activation status of anti-lock braking systems (ABS), a qualitative analysis method for assessing lateral and longitudinal excitation levels is proposed. Third, three simulation categories of under-excitations, moderate excitations, and over-excitations are set, encompassing nine typical operating conditions to validate the robustness and accuracy of the proposed TRPAC robust estimation algorithm. What’s more, a closed-loop simulation architecture incorporating TRPAC robust estimation, optimal slip ratio estimation, and ABS is established to validate the application effectiveness of the proposed algorithm. The simulation results demonstrate that the TRPAC robust estimation algorithm exhibits superior operational adaptability and robustness compared to the other five traditional estimation algorithms of TRPAC. Under moderate and over-excitation conditions, the maximum absolute error of the proposed algorithm remains below 0.05. The closed-loop simulations show that the proposed algorithm enhances ABS performance by increasing braking intensity by at least 1.1% and reducing braking distance by at least 1.8%.