GP-enhanced Autonomous Drifting Framework using ADMM-based iLQR
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2025
Publication Type
Conference Paper
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yes
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Abstract
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).
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published
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2025 American Control Conference (ACC)
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Pages / Article No.
3512 - 3519
Publisher
IEEE
Event
2025 American Control Conference (ACC 2025)
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01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning