Scholarlink

<p>This paper presents a torque vectoring control system that utilizes model predictive control (MPC) augmented by a Gaussian process (GP). Conventional MPC can suffer performance degradation when faced with un-modeled system dynamics or changing operating conditions. To address these limitations, the current work employs the GP to learn and compensate for residual vehicle dynamics and disturbances. The proposed framework features a dynamic online adaptation of the GP model, where its predictions are continuously refined based on recent driving data through a data buffering strategy and periodic hyperparameter re-optimization. This online learning framework, termed Streaming GP in this work, enhances overall system control accuracy and adaptability. The effectiveness of the proposed algorithm is demonstrated through comprehensive simulations on a vehicle model across various challenging driving scenarios, showing torque vectoring performance compared to conventional methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Data-driven Vehicle Torque Vectoring Control Using Streaming Gaussian Process MPC

  • 期刊论文

  • 发表日期：

    2025年12月5日

查看全文
  • Junghyo Kim,
  • Duc Giap Nguyen,
  • Suyong Park,
  • Minsoo Woo,
  • Daekwang Kim,
  • Kyoungseok Han

摘要

This paper presents a torque vectoring control system that utilizes model predictive control (MPC) augmented by a Gaussian process (GP). Conventional MPC can suffer performance degradation when faced with un-modeled system dynamics or changing operating conditions. To address these limitations, the current work employs the GP to learn and compensate for residual vehicle dynamics and disturbances. The proposed framework features a dynamic online adaptation of the GP model, where its predictions are continuously refined based on recent driving data through a data buffering strategy and periodic hyperparameter re-optimization. This online learning framework, termed Streaming GP in this work, enhances overall system control accuracy and adaptability. The effectiveness of the proposed algorithm is demonstrated through comprehensive simulations on a vehicle model across various challenging driving scenarios, showing torque vectoring performance compared to conventional methods.