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<p> To ensure accurate trajectory tracking and vehicle safety under extreme conditions such as emergency obstacle avoidance, this paper proposes an integrated control strategy based on dissipative energy (DE) for a four-wheel-drive intelligent electric vehicle (FWDIEV) equipped with active suspension. The strategy simultaneously addresses longitudinal, lateral trajectory tracking and lateral stability, as well as roll stability control. First, a four-degree-of-freedom (4-DOF) vehicle dynamics model is established. Lateral stability is analyzed using the DE method, with the stability threshold and ideal roll angle derived offline via a physics-informed neural network (PINN). Subsequently, a lateral stability constraint is formulated based on DE stability boundary solved online via the pre-trained PINN. Simultaneously, a roll stability contraction constraint is constructed via a Lyapunov function, incorporating with the ideal roll angle target. These constraints and target are embedded within a linear time-varying model predictive control (LTV-MPC) framework to coordinate front wheel steering angle, yaw moment, and roll moment for integrated control. Finally, wheel torques and active suspension forces are optimally allocated. Simulations and hardware-in-the-loop tests demonstrate that the strategy maintains the lateral position error below 0.197&#xa0;m and velocity error below 0.574&#xa0;m/s during a decelerated double-lane change maneuver, while significantly enhancing lateral and roll stability.</p>

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Integrated Control of Trajectory Tracking and Stability for FWDIEV Based on Dissipative Energy

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Kun Zhao,
  • Zhiguo Zhao,
  • Yutao Wang,
  • Xue Xia,
  • Liang Zhao,
  • Xi Chen

摘要

To ensure accurate trajectory tracking and vehicle safety under extreme conditions such as emergency obstacle avoidance, this paper proposes an integrated control strategy based on dissipative energy (DE) for a four-wheel-drive intelligent electric vehicle (FWDIEV) equipped with active suspension. The strategy simultaneously addresses longitudinal, lateral trajectory tracking and lateral stability, as well as roll stability control. First, a four-degree-of-freedom (4-DOF) vehicle dynamics model is established. Lateral stability is analyzed using the DE method, with the stability threshold and ideal roll angle derived offline via a physics-informed neural network (PINN). Subsequently, a lateral stability constraint is formulated based on DE stability boundary solved online via the pre-trained PINN. Simultaneously, a roll stability contraction constraint is constructed via a Lyapunov function, incorporating with the ideal roll angle target. These constraints and target are embedded within a linear time-varying model predictive control (LTV-MPC) framework to coordinate front wheel steering angle, yaw moment, and roll moment for integrated control. Finally, wheel torques and active suspension forces are optimally allocated. Simulations and hardware-in-the-loop tests demonstrate that the strategy maintains the lateral position error below 0.197 m and velocity error below 0.574 m/s during a decelerated double-lane change maneuver, while significantly enhancing lateral and roll stability.