Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control

TL;DR

This paper introduces a physics-guided deep reinforcement learning approach for real-time control of vehicle active suspension, significantly improving ride comfort and stability over traditional passive systems.

Contribution

It develops a novel DRL-based control system that respects physical constraints, optimizing suspension performance under diverse road conditions.

Findings

Reduces vehicle body velocity by 43.58%

Decreases acceleration by 17.22%

Enhances passenger comfort and vehicle stability

Abstract

The suspension system is a crucial part of the automotive chassis, improving vehicle ride comfort and isolating passengers from rough road excitation. Unlike passive suspension, which has constant spring and damping coefficients, active suspension incorporates electronic actuators into the system to dynamically control stiffness and damping variables. However, effectively controlling the suspension system poses a challenging task that necessitates real-time adaptability to various road conditions. This paper presents the Physics-Guided Deep Reinforcement Learning (DRL) for adjusting an active suspension system's variable kinematics and compliance properties for a quarter-car model in real time. Specifically, the outputs of the model are defined as actuator stiffness and damping control, which are bound within physically realistic ranges to maintain the system's physical compliance. The proposed model was trained on stochastic road profiles according to ISO 8608 standards to optimize the actuator's control policy. According to qualitative results on simulations, the vehicle body reacts smoothly to various novel real-world road conditions, having a much lower degree of oscillation. These observations mean a higher level of passenger comfort and better vehicle stability. Quantitatively, DRL outperforms passive systems in reducing the average vehicle body velocity and acceleration by 43.58% and 17.22%, respectively, minimizing the vertical movement impacts on the passengers. The code is publicly available at github.com/anh-nn01/RL4Suspension-ICMLA23.