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Related Experiment Video

Updated: Mar 15, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Published on: April 13, 2016

Real-Time Vibration Energy Prediction for Semi-Active Suspensions Using Inertial Sensors: A Physics-Guided Deep

Jian Cheng¹, Fanhua Qin¹, Leyao Wang¹

¹School of Mechanical and Electrical Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This study introduces a Physics-Informed Gated Convolutional Neural Network (PI-GCNN) for semi-active suspensions. The PI-GCNN predicts future road shock energy, enabling faster control responses and improved vehicle stability.

Keywords:

continuous wavelet transform embedded AI inertial sensors physics-guided deep learning semi-active suspension vibration energy prediction

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Area of Science:

Control Systems Engineering
Artificial Intelligence in Automotive Applications
Signal Processing

Background:

Response latency and sensor noise are critical challenges in closed-loop control systems, particularly affecting semi-active suspensions.
Physical actuation delays and signal filtering phase lag cause control responses to lag behind road shock excitations.

Purpose of the Study:

To develop a predictive control framework for semi-active suspensions to overcome latency issues.
To enable feedforward control by predicting future multi-modal energy evolution.

Main Methods:

Proposed a Physics-Informed Gated Convolutional Neural Network (PI-GCNN) utilizing Continuous Wavelet Transform (CWT) for time-frequency analysis.
Implemented a physics-guided gating mechanism trained with asymmetric sparse physics loss for noise suppression and impact sensitivity.
Validated the model using heavy truck simulations and the PVS 9 real-world dataset.

Main Results:

The PI-GCNN achieved a predictive phase lead of 100-200 ms over real-time baselines.
Demonstrated exceptional computational efficiency with 0.10 M parameters and 0.25 ms single-frame inference latency.
Successfully created a valuable actuation window for suspension dampers.

Conclusions:

The PI-GCNN effectively addresses latency and noise challenges in semi-active suspensions through predictive control.
The model's efficiency makes it suitable for resource-constrained automotive edge computing platforms.
This approach offers a significant advancement in enhancing vehicle dynamics and ride comfort.