Deep Learning-Based Prediction of Suspension Dynamics Performance in Multi-Axle Vehicles

TL;DR

This paper introduces a deep learning framework using multitask neural networks to accurately predict suspension dynamics in multi-axle vehicles, integrating traditional modeling with machine learning for improved performance assessment.

Contribution

The study develops a novel multitask deep belief network model that enhances suspension performance prediction accuracy and introduces the Suspension Dynamic Performance Index (SDPI) for comprehensive evaluation.

Findings

Deep learning model outperforms conventional DNNs in prediction accuracy.

Multitask learning improves the robustness of suspension performance predictions.

SDPI provides a holistic measure of suspension system effectiveness.

Abstract

This paper presents a deep learning-based framework for predicting the dynamic performance of suspension systems in multi-axle vehicles, emphasizing the integration of machine learning with traditional vehicle dynamics modeling. A Multi-Task Deep Belief Network Deep Neural Network (MTL-DBN-DNN) was developed to capture the relationships between key vehicle parameters and suspension performance metrics. The model was trained on data generated from numerical simulations and demonstrated superior prediction accuracy compared to conventional DNN models. A comprehensive sensitivity analysis was conducted to assess the impact of various vehicle and suspension parameters on dynamic suspension performance. Additionally, the Suspension Dynamic Performance Index (SDPI) was introduced as a holistic measure to quantify overall suspension performance, accounting for the combined effects of multiple parameters. The findings highlight the effectiveness of multitask learning in improving predictive models for complex vehicle systems.