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

arXiv: https://arxiv.org/abs/2408.08425 · HTML: https://mlsanity.com/paper/2408.08425/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 4 of 4)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [t-Soft Update of Target Network for Deep Reinforcement Learning](https://mlsanity.com/paper/2008.10861/) (2020)
- [A Deep Reinforcement Learning-Based Controller for Magnetorheological-Damped Vehicle Suspension](https://mlsanity.com/paper/2301.02714/) (2023)

## Built on by (most cited first, 2 of 2)

- [Time-Aware World Model for Adaptive Prediction and Control](https://mlsanity.com/paper/2506.08441/) (2025)
- [Self driving algorithm for an active four wheel drive racecar](https://mlsanity.com/paper/2506.06077/) (2025)

## Abstract (arXiv)

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.