发表机构
Drive System Design（驱动系统设计）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究液压离合器控制回路压力响应建模，通过扩展输入向量、测试分类器，用高斯过程回归模型拟合，经实验评估，该机器学习模型比基于物理的模拟更能准确再现压力响应和滞后行为，可补充相关模型。
AI 中文摘要
本文提出一种数据驱动方法对液压离合器控制回路的压力响应进行建模。该系统由可变力螺线管、蓄能器、压力调节阀和锁止阀组成，存在滞后、锁止转换和执行器动力学引起的非线性行为。基于指令电流变量的基线模型能捕捉一般压力响应，但无法准确表示滞后和锁止行为。因此用电流导数信息扩展输入向量，并在拟合高斯过程回归模型前测试多个分类器以分离与锁止相关的运行状态。非线性SVC和梯度提升产生了最高的锁存分类精度，并选择非线性SVC用于最终的局部回归管道。将该方法在未见斜坡率数据上进行评估，并与基于物理的Amesim模型进行比较。在测试工况下，机器学习模型比基于物理的模拟更准确地再现了测量的压力响应和滞后行为。这些结果表明，当有代表性的试验台数据时，机器学习对象模型可在硬件开发和控制器校准过程中补充基于物理的液压模型。
英文摘要
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.
Comments8 pages, 5 figures. Accepted to the program of the 14th CTI Symposium and Exhibition, Automotive Drivetrains, Intelligent, Electrified, scheduled for May 13-14, 2020 in Novi, Michigan, USA
