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<p>This study proposes an advanced antilock brake system (ABS) control strategy that significantly enhanced braking performance by incorporating real-time feedback from Wheel Brake Pressure Sensors (WBPS). Unlike conventional ABS systems that rely solely on wheel speed and acceleration signals, the proposed approach leverages WBPS to directly estimate applied brake torque and improve road friction estimation accuracy. By integrating pressure-based torque estimation with an instantaneous least square (LS) friction estimator, the system dynamically identifies the road surface condition and adjusts braking force to maintain optimal slip ratios. An adaptive sliding mode control (ASMC) algorithm is employed to robustly track target wheel speeds based on estimated peak slip, ensuring stable deceleration and wheel slip regulation even under severe low-<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:\mu\:\)</EquationSource> </InlineEquation> conditions. Simulation results demonstrate that the WBPS-integrated ABS achieves substantial improvements in braking performance, including reduced stopping distance, avoidance of wheel lock-up, and enhanced stability on friction transition and split-friction roads. These findings are further validated through full-vehicle experiments on snow, ice, and mixed surfaces, confirming the real-world effectiveness of WBPS in adaptive brake control. The results highlight the potential of sensor-augmented model-based ABS architectures for next-generation intelligent braking systems.</p>

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Model-Based Antilock Brake System with Integrated Wheel Brake Pressure Sensing for Robust Slip Control

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Kwang Hyun Cho,
  • Sei Bum Choi,
  • Kang Hyun Nam

摘要

This study proposes an advanced antilock brake system (ABS) control strategy that significantly enhanced braking performance by incorporating real-time feedback from Wheel Brake Pressure Sensors (WBPS). Unlike conventional ABS systems that rely solely on wheel speed and acceleration signals, the proposed approach leverages WBPS to directly estimate applied brake torque and improve road friction estimation accuracy. By integrating pressure-based torque estimation with an instantaneous least square (LS) friction estimator, the system dynamically identifies the road surface condition and adjusts braking force to maintain optimal slip ratios. An adaptive sliding mode control (ASMC) algorithm is employed to robustly track target wheel speeds based on estimated peak slip, ensuring stable deceleration and wheel slip regulation even under severe low- \(\:\mu\:\) conditions. Simulation results demonstrate that the WBPS-integrated ABS achieves substantial improvements in braking performance, including reduced stopping distance, avoidance of wheel lock-up, and enhanced stability on friction transition and split-friction roads. These findings are further validated through full-vehicle experiments on snow, ice, and mixed surfaces, confirming the real-world effectiveness of WBPS in adaptive brake control. The results highlight the potential of sensor-augmented model-based ABS architectures for next-generation intelligent braking systems.