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<p>This paper addresses the coordination control problem between the anti-lock braking system (ABS) and the electronic stability program (ESP) in vehicle active safety systems and proposes a hierarchical control strategy based on intelligent algorithms. First, a pavement recognition system is developed using an improved BP neural network algorithm based on the Burckhardt tire model, which achieves high-precision online identification of various pavement friction coefficients. Real vehicle experiments on in-wheel motor prototypes validate the results, with a maximum identification error of no more than 0.01, providing accurate road condition information for the ABS. Next, a global sliding mode controller based on an improved double-power approaching law is developed for the ABS, ensuring global stability while effectively reducing vibration amplitude. This shortens braking distance and time by about 3&#xa0;m and 0.2&#xa0;s, respectively. For the ESP system, the whale optimization algorithm is applied to achieve real-time self-tuning of PID parameters, resulting in approximately 10% and 40% reductions in longitudinal speed and displacement amplitude under serpentine conditions. Simulation results show that the proposed ABS/ESP coordination control strategy performs excellently under complex conditions, reducing maximum lateral acceleration by 80% and maintaining slip ratio near optimal values, thereby significantly enhancing vehicle braking safety and handling stability. This research provides an innovative technical solution for developing active safety systems in intelligent vehicles.</p>

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Coordination control strategy of anti-lock braking system/electronic stability program using improved global sliding mode control and whale optimization PID algorithm

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Mingxin Chen,
  • Xiaobin Fan,
  • Zipeng Huang,
  • Xueliang Yu,
  • Linhui Wang

摘要

This paper addresses the coordination control problem between the anti-lock braking system (ABS) and the electronic stability program (ESP) in vehicle active safety systems and proposes a hierarchical control strategy based on intelligent algorithms. First, a pavement recognition system is developed using an improved BP neural network algorithm based on the Burckhardt tire model, which achieves high-precision online identification of various pavement friction coefficients. Real vehicle experiments on in-wheel motor prototypes validate the results, with a maximum identification error of no more than 0.01, providing accurate road condition information for the ABS. Next, a global sliding mode controller based on an improved double-power approaching law is developed for the ABS, ensuring global stability while effectively reducing vibration amplitude. This shortens braking distance and time by about 3 m and 0.2 s, respectively. For the ESP system, the whale optimization algorithm is applied to achieve real-time self-tuning of PID parameters, resulting in approximately 10% and 40% reductions in longitudinal speed and displacement amplitude under serpentine conditions. Simulation results show that the proposed ABS/ESP coordination control strategy performs excellently under complex conditions, reducing maximum lateral acceleration by 80% and maintaining slip ratio near optimal values, thereby significantly enhancing vehicle braking safety and handling stability. This research provides an innovative technical solution for developing active safety systems in intelligent vehicles.