用高斯过程修正模型误差，让赛车在极限漂移中更精准控制。
Learning to Drift in Extreme Turning with Active Exploration and Gaussian Process Based MPC
- 用高斯过程动态修正车辆模型偏差，提升控制精度。
- 仿真中侧向误差降低52.8%，实验中减少36.7%。
- 主动探索策略进一步降低误差，适合自动驾驶赛车控制研究。
赛车在极限弯道中常出现大侧滑角，传统控制器难以应对，需采用漂移控制。但大侧滑导致模型失配，影响控制精度。本文提出一种结合模型预测控制（MPC）与高斯过程回归（GPR）的漂移控制器，利用GPR在漂移平衡求解和MPC优化中实时修正模型误差，并通过GPR方差引导主动探索不同漂移速度，以最小化轨迹跟踪误差。在Simulink-Carsim平台仿真和1:10比例遥控车实验中验证：仿真下，引入GPR使平均侧向误差降低52.8%，加入探索后进一步减少27.1%；速度跟踪均方根误差（RMSE）下降10.6%。实车实验中，侧向误差降低36.7%，探索再降29.0%，速度跟踪RMSE减少7.2%。
原文摘要 · Abstract (English)
Extreme cornering in racing often leads to large sideslip angles, presenting a significant challenge for vehicle control. Conventional vehicle controllers struggle to manage this scenario, necessitating the use of a drifting controller. However, the large sideslip angle in drift conditions introduces model mismatch, which in turn affects control precision. To address this issue, we propose a model correction drift controller that integrates Model Predictive Control (MPC) with Gaussian Process Regression (GPR). GPR is employed to correct vehicle model mismatches during both drift equilibrium solving and the MPC optimization process. Additionally, the variance from GPR is utilized to actively explore different cornering drifting velocities, aiming to minimize trajectory tracking errors. The proposed algorithm is validated through simulations on the Simulink-Carsim platform and experiments with a 1:10 scale RC vehicle. In the simulation, the average lateral error with GPR is reduced by 52.8% compared to the non-GPR case. Incorporating exploration further decreases this error by 27.1%. The velocity tracking Root Mean Square Error (RMSE) also decreases by 10.6% with exploration. In the RC car experiment, the average lateral error with GPR is 36.7% lower, and exploration further leads to a 29.0% reduction. Moreover, the velocity tracking RMSE decreases by 7.2% with the inclusion of exploration.
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