GPとADMMベースiLQRを用いた自動運転ドリフト制御フレームワーク
GP-enhanced Autonomous Drifting Framework using ADMM-based iLQR
ガウス過程回帰でモデル誤差を補償し、ADMMベースのiLQRでリアルタイムに軌道最適化を行う自動ドリフト制御手法を提案し、横方向誤差と計算時間を大幅に削減した。
著者: Yangyang Xie, Cheng Hu, Nicolas Baumann, Edoardo Ghignone, Michele Magno, Lei Xie
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Autonomous drifting is a complex challenge due to the highly nonlinear dynamics and the need for precise real-time control, especially in uncertain environments. To address these limitations, this paper presents a hierarchical control framework for autonomous vehicles drifting along general paths, primarily focusing on addressing model inaccuracies and mitigating computational challenges in real-time control. The framework integrates Gaussian Process (GP) regression with an Alternating Direction Method of Multipliers (ADMM)-based iterative Linear Quadratic Regulator (iLQR). GP regression effectively compensates for model residuals, improving accuracy in dynamic conditions. ADMM-based iLQR not only combines the rapid trajectory optimization of iLQR but also utilizes ADMM's strength in decomposing the problem into simpler sub-problems. Simulation results demonstrate the effectiveness of the proposed framework, with significant improvements in both drift trajectory tracking and computational efficiency. Our approach resulted in a 38$\%$ reduction in RMSE lateral error and achieved an average computation time that is 75$\%$ lower than that of the Interior Point OPTimizer (IPOPT).
