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<p>This paper proposes a sliding mode control (SMC) strategy based on particle swarm optimization (PSO) to simultaneously optimize path tracking accuracy and handling stability for four-wheel steer-by-wire autonomous vehicles. First, a front-wheel sliding mode controller is designed based on the vehicle error model, achieving precise path tracking by integrating lateral and heading errors. Second, a stability controller is developed based on SMC theory, which enhances vehicle handling stability by adjusting the rear wheel steering angle using the ideal yaw rate and sideslip angle as reference targets. Subsequently, the PSO algorithm is introduced, and a weighted normalized fitness function incorporating sideslip angle error, lateral error, and yaw rate error is employed to globally optimize key SMC parameters. Finally, collaborative simulation with CarSim and MATLAB/Simulink under double lane change (DLC) and circular path conditions validates the effectiveness and robustness of the proposed strategy. Simulation results demonstrate that compared to MPC, traditional SMC, and PID control, this strategy significantly reduces tracking errors. Under DLC conditions with a road surface coefficient of 0.85, the maximum lateral error is reduced by 10.58%, 48.96%, and 77.17%, and the maximum heading error by 27.13%, 47.70%, and 63.90%, respectively. Under low-adhesion conditions, the PSO + SMC strategy demonstrates optimal path-tracking capability and outstanding stability, proving its robustness. Under circular path conditions, the maximum lateral error is reduced by 71.33%, 78.50%, and 87.93%, and the maximum heading error by 20.00%, 8.49%, and 50.38%, respectively. Additionally, the proposed strategy brings the vehicle’s yaw rate and sideslip angle closer to their ideal values, effectively enhancing stability. These results confirm that the proposed control strategy significantly improves both path tracking accuracy and handling stability.</p>

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Path tracking and stability control of four-wheel steer-by-wire vehicles using sliding mode control based on particle swarm optimization

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Zhu An Zheng,
  • Qiyuan Huang,
  • Shuangjian Xie,
  • Zhiwei Yu,
  • Yujie Ni,
  • Haoyu Wang

摘要

This paper proposes a sliding mode control (SMC) strategy based on particle swarm optimization (PSO) to simultaneously optimize path tracking accuracy and handling stability for four-wheel steer-by-wire autonomous vehicles. First, a front-wheel sliding mode controller is designed based on the vehicle error model, achieving precise path tracking by integrating lateral and heading errors. Second, a stability controller is developed based on SMC theory, which enhances vehicle handling stability by adjusting the rear wheel steering angle using the ideal yaw rate and sideslip angle as reference targets. Subsequently, the PSO algorithm is introduced, and a weighted normalized fitness function incorporating sideslip angle error, lateral error, and yaw rate error is employed to globally optimize key SMC parameters. Finally, collaborative simulation with CarSim and MATLAB/Simulink under double lane change (DLC) and circular path conditions validates the effectiveness and robustness of the proposed strategy. Simulation results demonstrate that compared to MPC, traditional SMC, and PID control, this strategy significantly reduces tracking errors. Under DLC conditions with a road surface coefficient of 0.85, the maximum lateral error is reduced by 10.58%, 48.96%, and 77.17%, and the maximum heading error by 27.13%, 47.70%, and 63.90%, respectively. Under low-adhesion conditions, the PSO + SMC strategy demonstrates optimal path-tracking capability and outstanding stability, proving its robustness. Under circular path conditions, the maximum lateral error is reduced by 71.33%, 78.50%, and 87.93%, and the maximum heading error by 20.00%, 8.49%, and 50.38%, respectively. Additionally, the proposed strategy brings the vehicle’s yaw rate and sideslip angle closer to their ideal values, effectively enhancing stability. These results confirm that the proposed control strategy significantly improves both path tracking accuracy and handling stability.