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<p>As autonomous driving technology advances, vehicle control is shifting from driver-centric approaches to algorithm-based frameworks. This study develops a method to control vehicle roll behavior by utilizing preview information obtained from the autonomous driving system. Specifically, future steering inputs from the vehicle’s path-planning algorithm are used to proactively suppress roll motion through an active suspension-based anti-roll control strategy. The proposed controller employs a speed-dependent, 3 DOF roll-steer model that includes actuator delay dynamics to reflect practical implementation constraints. A model predictive control (MPC) approach is adopted to compute optimal suspension forces that minimize roll angle and roll rate, while also satisfying constraints on actuator force magnitude and rate of change. The control strategy was validated through CarMaker–Simulink co-simulations. Results demonstrate that using preview information significantly improves ride comfort compared to conventional MPC without preview information. The proposed framework is adaptable with various steering prediction and speed control strategies, highlighting the potential benefits of integrating autonomous driving with active chassis control.</p>

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Preview Anti-roll Control with Actuator Delay in Autonomous Driving

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Junyoung Jung,
  • Minsang Park,
  • Chibum Lee

摘要

As autonomous driving technology advances, vehicle control is shifting from driver-centric approaches to algorithm-based frameworks. This study develops a method to control vehicle roll behavior by utilizing preview information obtained from the autonomous driving system. Specifically, future steering inputs from the vehicle’s path-planning algorithm are used to proactively suppress roll motion through an active suspension-based anti-roll control strategy. The proposed controller employs a speed-dependent, 3 DOF roll-steer model that includes actuator delay dynamics to reflect practical implementation constraints. A model predictive control (MPC) approach is adopted to compute optimal suspension forces that minimize roll angle and roll rate, while also satisfying constraints on actuator force magnitude and rate of change. The control strategy was validated through CarMaker–Simulink co-simulations. Results demonstrate that using preview information significantly improves ride comfort compared to conventional MPC without preview information. The proposed framework is adaptable with various steering prediction and speed control strategies, highlighting the potential benefits of integrating autonomous driving with active chassis control.