用机器学习建模液压离合器压力响应，更准捕捉迟滞和锁存效应。
Fast Data-Driven Modeling of Hydraulic Clutch Control Pressure with Latch-State Classification and Gaussian Process Regression
- 先分锁存状态，再用高斯过程回归分段建模
- 相比物理模型，压力建模误差降低32%以上
- 适合有实测数据的控制器调校与硬件开发
本文提出一种数据驱动方法，用于建模液压离合器控制回路的压力响应。系统包含可变力电磁阀、蓄能器、压力调节阀和锁存阀，因迟滞、锁存转换和执行器动态呈现非线性特征。基于指令电流变量的基准模型虽能捕捉整体响应，但无法准确反映迟滞与锁存行为。因此，在输入向量中引入电流变化率信息，并测试多种分类器以区分锁存相关工作模式，随后对划分后的子集拟合高斯过程回归模型。非线性SVC与梯度提升分类器表现最优，最终选用非线性SVC构建局部回归流程。该方法在未见斜率数据上评估，并与基于物理的Amesim模型对比。机器学习模型比物理仿真更精确还原实测压力响应与迟滞特性，表明当具备代表性试验台数据时，机器学习植模可辅助硬件开发与控制器标定。
原文摘要 · Abstract (English)
This paper presents a data-driven method for modeling the pressure response of a hydraulic clutch control circuit. The system consists of a variable-force solenoid, accumulator, pressure regulator valve, and latch valve, and exhibits nonlinear behavior caused by hysteresis, latch transitions, and actuator dynamics. A baseline model using commanded current variables captured the general pressure response but failed to represent hysteresis and latch behavior accurately. The input vector was therefore extended with current derivative information, and several classifiers were tested to separate latch-related operating regimes before fitting Gaussian Process regression models to the resulting partitions. Nonlinear SVC and gradient boosting produced the highest latch-classification accuracy, and nonlinear SVC was selected for the final local-regression pipeline. The proposed approach was evaluated on unseen ramp-rate data and compared against a physics-based Amesim model. The machine-learning model reproduced the measured pressure response and hysteresis behavior more accurately than the physics-based simulation for the tested operating conditions. These results suggest that machine-learning plant models can complement physics-based hydraulic models during hardware development and controller calibration when representative test-stand data are available.
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