Fast Data-Driven Modeling of Hydraulic Clutch Control Pressure with Latch-State Classification and Gaussian Process Regression
2026-07-11 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new method using data and machine learning to better predict pressure changes in a hydraulic clutch system, which behaves in complex ways. Their initial model struggled to capture certain effects like hysteresis and latch transitions, so they improved it by adding information about how the input changes over time and by classifying different operating states. Using advanced classification and regression techniques, their improved model predicted pressure responses more accurately than a traditional physics-based simulation. This shows that machine learning models can help improve system understanding and calibration when good test data are available.
hydraulic clutchhysteresissolenoidpressure regulator valveGaussian Process regressionsupport vector classifiergradient boostingmachine learningphysics-based modelingactuator dynamics
Authors
Yash Bagla, Jason Schneider
Abstract
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.