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Machines
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Advances in Vehicle Suspension System Optimization and Control
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Advances in Vehicle Suspension System Optimization and Control

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2919

Special Issue Editors

Dr. Buyun Zhang

E-Mail Website
Guest Editor
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China
Interests: vibration test and control; NVH test and control; modal analysis with its application; structure optimal design for lightweight
Prof. Dr. Chin-An Tan

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Wayne State University, Detroit, MI, USA
Interests: structural dynamics and control; advanced vehicle mobility solutions; autonomous/intelligent robotic systems; intelligence in materials

Special Issue Information

Dear Colleagues,

As the automotive industry evolves, the demand for enhanced vehicle performance, safety, and passenger comfort has led to significant innovations in suspension technologies. It is important to explore and highlight the latest advancements in optimizing and controlling vehicle suspension systems of passive, semi-active, or active vehicle suspension systems.

This Special Issue aims to delve into novel methodologies and approaches that address these challenges, offering insights into both theoretical and practical aspects. Contributions exploring the application of machine learning and artificial intelligence in predicting suspension behavior and optimizing system parameters are particularly welcome. Additionally, we encourage discussions on integrating advanced materials and smart technologies that contribute to improved performance and efficiency.

We are pleased to invite you to submit original research articles, reviews, and case studies that focus on, but not limited to, the following topics:

  • Metrics and methodologies for evaluating suspension systems;
  • Ride comfort, handling, and safety;
  • Complex interactions between vehicle dynamics and suspension characteristics;
  • Electrification and automation in suspension design and control;
  • Experimental validations, simulations, and real-world applications;
  • Impact of Vehicle–Bridge/Road Interaction (VBI or VRI) on vehicle suspension systems;
  • Machine learning and AI in suspension systems;
  • Energy-related designs, e.g., energy-regenerative and energy-efficient systems;
  • Advanced suspension systems for smart transportation;
  • Suspension systems considering the train-track interactions;
  • Nonlinear suspension systems. 

We look forward to your contributions, which will drive the next generation of vehicle dynamics research.

Dr. Buyun Zhang
Prof. Dr. Chin-An Tan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ride comfort
  • energy-related
  • active suspension
  • semi-active suspension
  • passive suspension
  • nonlinear
  • vehicle road/bridge interactions
  • machine learning

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Published Papers (5 papers)

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Research

22 pages, 11397 KiB  
Article
Road Roughness Recognition: Feature Extraction and Speed-Adaptive Classification Based on Simulation and Real-Vehicle Tests
by Jie Xing, Zhun Cheng, Shuai Ye, Songwei Liu and Jiawei Lin
Machines 2025, 13(5), 391; https://doi.org/10.3390/machines13050391 - 8 May 2025
Viewed by 207
Abstract
Road roughness exerts a direct influence on the vertical dynamic performance of vehicles, and the accurate characterization of road roughness is essential for optimizing vehicle suspension systems. This paper addresses two key challenges in roughness recognition: feature extraction and adaptive classification under different [...] Read more.
Road roughness exerts a direct influence on the vertical dynamic performance of vehicles, and the accurate characterization of road roughness is essential for optimizing vehicle suspension systems. This paper addresses two key challenges in roughness recognition: feature extraction and adaptive classification under different speeds. In detail, based on simulation tests of the quarter-vehicle vertical vibration model and real-vehicle test, this paper reveals the strong correlation between the unsprung mass vertical vibration response of vehicles and road roughness. The feasibility of using unsprung mass vertical vibration response as a feature for recognizing and classifying road roughness is verified. And an adaptive road roughness classifier is proposed based on vehicle-speed-related features. Both simulation and real-vehicle results confirm that (i) the unsprung vertical vibration displacement is strongly correlated with road roughness (R2 = 0.997); (ii) road roughness can be classified with high accuracy with the unsprung mass vertical vibration response taken as the only feature (simulation tests: 98.88% to 100%; real-vehicle tests: 100%); and (iii) the accuracy of the proposed speed-adaptive classifier is 20% more accurate than the conventional classifier that does not consider vehicle speed features. This research can provide accurate road excitation for the adaptive real-time control of semi-active or active vehicle suspensions. Full article
(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)
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23 pages, 4223 KiB  
Article
Trajectory Tracking and Driving Torque Distribution Strategy for Four-Steering-Wheel Heavy-Duty Automated Guided Vehicles
by Xia Li, Xiaojie Chen, Shengzhan Chen, Benxue Liu and Chengming Wang
Machines 2025, 13(5), 383; https://doi.org/10.3390/machines13050383 - 1 May 2025
Viewed by 192
Abstract
A four-steering-wheel heavy-duty Automated Guided Vehicle (AGV) is prone to lateral instability and wheel slippage during acceleration, climbing, and small-radius turns. To address this issue, a trajectory tracking strategy considering lateral stability and an optimal driving torque distribution strategy considering load transfer and [...] Read more.
A four-steering-wheel heavy-duty Automated Guided Vehicle (AGV) is prone to lateral instability and wheel slippage during acceleration, climbing, and small-radius turns. To address this issue, a trajectory tracking strategy considering lateral stability and an optimal driving torque distribution strategy considering load transfer and tire adhesion coefficient are proposed. Firstly, a three-degree-of-freedom AGV trajectory tracking model is established, tracking error and sideslip angle are incorporated into the cost function, and an improved model predictive trajectory tracking controller is proposed. Secondly, the longitudinal and yaw dynamic model of AGV is established, and vertical load transfer is analyzed. With the goal of minimizing tire adhesion utilization rate, quadratic programming is used for the optimal distribution of driving torque. Finally, through co-simulation using ADAMS and MATLAB on a narrow “climbing straight+ S-curve” road, the maximum tracking error is 0.0443 m. Compared to the unimproved model predictive control and average driving torque distribution strategy, the sideslip angle is reduced by 58.18%, the maximum tire adhesion utilization rate is reduced by 6.62%, and climbing gradeability on wet roads is enhanced. Full article
(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)
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39 pages, 29772 KiB  
Article
Improving Vehicle Dynamics: A Fractional-Order PIλDμ Control Approach to Active Suspension Systems
by Zongjun Yin, Chenyang Cui, Ru Wang, Rong Su and Xuegang Ma
Machines 2025, 13(4), 271; https://doi.org/10.3390/machines13040271 - 25 Mar 2025
Viewed by 278
Abstract
This paper presents a comprehensive vehicle model featuring an active suspension system integrated with semi-active seat and engine mounting controls. The time-domain stochastic excitation of the four tires was modeled using the filtered white noise method, and the required road excitation was simulated [...] Read more.
This paper presents a comprehensive vehicle model featuring an active suspension system integrated with semi-active seat and engine mounting controls. The time-domain stochastic excitation of the four tires was modeled using the filtered white noise method, and the required road excitation was simulated using MATLAB software R2022b. Four comprehensive performance indices, including engine dynamic displacement, vehicle body acceleration, suspension dynamic deflection, and tire dynamic displacement, were selected and made dimensionless by the performance indices of a passive suspension under the same working conditions to construct the fitness function. A fractional-order PIλDμ (FOPID) controller was proposed, and its structural parameters were optimized using a gray wolf optimization algorithm. Furthermore, the optimized FOPID controller was evaluated under five road conditions, and its performance was compared with integer-order PID control and passive suspensions. The results demonstrate that the FOPID controller effectively improves the smoothness of the vehicle by reducing engine mounting deflection, vehicle body acceleration, suspension deflection, and tire displacement. Moreover, the simulation results indicate that, compared to the passive suspension, the FOPID-controlled suspension achieves an average optimization of over 42% in the root mean square (RMS) of body acceleration under random road conditions, with an average optimization of more than 38% for suspension deflection, 4.3% for engine mounting deflection, and 2.5% for tire displacement. In comparison to the integer-order PID-controlled suspension, the FOPID-controlled suspension demonstrates an average improvement of 28% in the RMS of acceleration and a 2.1% improvement in suspension deflection under random road conditions. However, the engine mounting deflection and tire displacement are reduced by 0.05% and 0.3%, respectively. FOPID control has better performance in vehicle acceleration control but shows asymmetrical effects on tire dynamic deflection. Full article
(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)
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20 pages, 12256 KiB  
Article
Enhanced Seat Suspension Performance Through Positive Real Network Optimization and Skyhook Inertial Control
by Xiaofeng Yang, Rui Sun, Yi Yang, Yanling Liu, Jingchen Hong and Changning Liu
Machines 2025, 13(3), 222; https://doi.org/10.3390/machines13030222 - 8 Mar 2025
Cited by 1 | Viewed by 392
Abstract
To solve the low frequency vibration problem faced by heavy truck drivers, a positive real network inertial suspension structure combined with a skyhook inertial control strategy is adopted. This integrated approach effectively reduces low-frequency vibrations at the seat and human body levels. Specifically, [...] Read more.
To solve the low frequency vibration problem faced by heavy truck drivers, a positive real network inertial suspension structure combined with a skyhook inertial control strategy is adopted. This integrated approach effectively reduces low-frequency vibrations at the seat and human body levels. Specifically, this research aims to mitigate the acceleration experienced on the seat surface within the low-frequency range. Firstly, a human–seat dynamics model is established. Subsequently, based on the principles of network synthesis, the derivation of transfer functions for both first- and second-order systems is discussed, and the network parameters are also optimized. This paper further compares the optimization outcomes of first- and second-order skyhook seat inertial suspensions. An adaptive fuzzy sliding-mode controller (AFSMC) has been developed for an electromechanical inerter, ensuring it closely tracks optimal control performance. The findings demonstrate that the new suspension system achieves a 29.9% reduction in the root-mean-square value of seat surface acceleration and a 43.1% decrease in the road-bump peak acceleration compared to a conventional suspension system. The results show that the inertial suspension with skyhook inertial control is highly effective in completely suppressing seat surface acceleration within the low-frequency domain. Full article
(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)
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20 pages, 5927 KiB  
Article
Design of Active Suspension Controllers for 8 × 8 Armored Combat Vehicles
by Yonghwan Jeong and Seongjin Yim
Machines 2024, 12(12), 931; https://doi.org/10.3390/machines12120931 - 18 Dec 2024
Cited by 1 | Viewed by 1092
Abstract
This paper presents a method to design an active suspension controller for 8 × 8 armored combat vehicles, which is called corner damping control (CDC). It is assumed that the target vehicle with 8 × 8 drive mechanisms and 8 suspensions has active [...] Read more.
This paper presents a method to design an active suspension controller for 8 × 8 armored combat vehicles, which is called corner damping control (CDC). It is assumed that the target vehicle with 8 × 8 drive mechanisms and 8 suspensions has active actuators on each suspension for vertical, roll and pitch motion control on a sprung mass. A state-space model with 22 state variables is derived from the target vehicle. With the state-space model, a linear quadratic (LQ) cost function is defined. The control objective is to reduce the vertical acceleration, pitch and roll angles of a sprung mass for ride comfort, durability and turret stabilization. To avoid full-state feedback of LQR, a static output feedback control (SOF) is selected as a control structure for CDC. The vertical velocity, roll and pitch rates of a sprung mass, and vertical velocities at each corner, are selected as a sensor output. With those sensor outputs and LQ cost function, four LQ SOF controllers are designed. To validate the effectiveness of the LQ SOF controllers, simulation is carried out on a vehicle simulation package. From the simulation results, it is shown that the proposed CDC with LQ SOF controllers with a much smaller number of sensor outputs and controller gains can reduce the vertical acceleration, pitch and roll angles of a sprung mass and, as a result, improve ride comfort, durability and turret stabilization. Full article
(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)
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