適応型微分可能モデル予測輪郭制御による自動運転レース
AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing
路面状態の変化に対応するため、オンラインでパラメータ推定と目的関数の重み調整を行う適応型MPCCを提案し、シミュレーションで安全性と速いラップタイムを実証した。
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著者: Nam T. Nguyen, Binh Nguyen, Ahmad Amine, Thanh Vo-Duy, Rahul Mangharam, Truong X. Nghiem
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme with exponentially decaying weights to capture road interactions and update parameters in real time. Furthermore, we propose a differentiable MPCC (Diff-MPCC) framework that enables optimal adjustment of objective weights based on predefined long-horizon performance costs. To implement Diff-MPCC for online objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner using data generated by Diff-MPCC to tune the objective weights. Simulation results demonstrate that AD-MPCC reliably ensures safety and achieves faster lap times compared to baseline controllers in both single-surface and multiple-surface scenarios.
