High-Agility Maneuvering in Distributed-Drive Electric Vehicles via a Hierarchical Framework

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Abstract
Distributed-drive electric vehicles (DDEVs) offer strong agility but face challenges in achieving fast dynamic response and robust stability under demanding conditions. To address this, we propose a hierarchical motion control framework that decouples path-following from attitude stabilization. A fixed-time extended state observer (FTESO) is designed to mitigate disturbance estimation errors from model inaccuracies, while a fractional-order sliding mode control (FOSMC) ensures accurate trajectory tracking. A multiobjective optimization model incorporating dynamic constraints and actuator limits is formulated and transformed into a constrained quadratic programming (QP) problem. Experiments on a 1:5 scaled chassis demonstrator validate the approach, showing a 42% reduction in lateral tracking error and sub-5-ms response time, outperforming state-of-the-art methods.
Funding Information
  • Jiangsu Provincial Key Research and Development Program (BF2025021)
  • National Natural Science Foundation of China (62222307)
  • National Natural Science Foundation of China (62573114)

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