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Stability-constrained coordinated control strategy for vehicle chassis integrated AFS and DYC via reinforcement learning
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DOI：10.1016/j.conengprac.2026.107122.png)
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Abstract
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Distributed cooperative control enhances the handling and dynamic response of autonomous vehicle, effectively addressing dynamic coupling effects among multiple actuators while reducing overall system complexity, which enables globally optimized cooperative control for X-by-wire chassis systems. So, this paper proposes a novel distributed coordinated control strategy based on reinforcement learning (RL) to dynamically balance the active front wheel steering system (AFS) and direct yaw moment control system (DYC). First, distributed state equations are formulated, and the information exchange process among agents is established. Second, the control objectives of each subsystem are defined and a cost function incorporating terminal and control constraints is designed. Then, the stability index is redefined by introducing yaw rate constraints into the phase plane, which together with subsystem tracking performance is used for the RL reward designed. This enables dynamic adjustment of the AFS and DYC control weights, achieving more efficient stability control while satisfying the trajectory constraints. Finally, co-simulation and hardware-in-loop (HIL) experiments are conducted to evaluate the control performance and experiment results validate the effectiveness of the proposed strategy for coordinated vehicle chassis control.
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