A Review on Controlling Methods for Semi-Active Suspension Systems

Abstract

In recent years the need of vehicle safety and ride comfort has led to develop effective modeling and control methods of suspension system. There are many methods already found to develop and control this kind of suspension systems. Mainly there are three types of suspension systems, Active, Semi-Active and Passive suspensions. This research paper presents a comprehensive review of controlling methods employed in semi-active suspension systems, a critical component in modern vehicle dynamics. Out of these three, semi-active suspension has the ability to adapt with the technical changes in the system model. In this paper some of the control methods for the semi-active suspension system have been discussed and a quick review is done on their way of operation. Moreover, the paper addresses recent advancements in semi-active suspension control, such as the integration of machine learning and optimization techniques.

Introduction

I. INTRODUCTION

There are different kind of performance levels in terms of road handling, vibration isolation, and ride comfort that should be ensured by the suspension system of the vehicle. On the basis of external power source input, the vehicle suspensions can be classified mainly into three types:

A. Semi-active, Active and Passive

The Semi-active suspensions have been the main focus of research because of their better performance than passive suspension and lower energy consumption than active ones. [1]

Main components of this kind of semi-active suspension system consist of the spring and the dampers. By reducing the stiffness and damping of this suspension system can help in shock absorption and vibration reduction improving the vehicle stability and ride comfort of passengers. [2]

These semi-active suspensions have become popular in automotive field because of their cost-effective solutions that can be utilized to significantly to increase driving comfort. The initial technology for semi-active suspensions was the so-called linear adaptive configuration. In recent years various approaches and methods have been proposed to model and control these kinds of systems, such as- optimal control (Poussot-Vassal et al., 2006), MPC (Canale et al., 2006), LPV (Poussot-Vassal et al., 2008).[4] Some of which we have tried to review here.

B. Multi-Modes Control of Semi-active Suspension by CVD

As per author, G. BEL HAJ FREG et.al (2020) a continuously variable damper (CVD) is used for design and analysis of a multi-mode semi-active suspension for a quarter car. Here for the quarter car model three modes of suspension have been developed and a method of modelling for the CVD is presented. The damper units here used is composed of passive metallic spring which has associated with the actuators to provide high vehicle regulation.

As the investigated damper's damping adjustment is more effective so it can achieve various suspension modes by adjusting the actuation force. Both ride comfort and driving safety can be achieved by applying a suitable control strategy to the damper by minimizing the quadratic gap between the control actuation force for each mode and a control target. The CRONE-SkyHook approach is used to synthesize the control target. For this method effectiveness is confirmed using a real measured road profile and a speed bump profile.

B. Vibration Control of Semi-active Suspension by Neural Network

The Author, Kaoru Sato et.al suggests an approach to control semi-active suspension which takes into account the forward road surface geometry. Using an MLD (Mixed Logical Dynamical) model which represents a vehicle model with a semi-active suspension, information about potential disturbances for road ahead can be obtained prior to the vehicle experiencing them.

Here they formulated MIQP (Mixed Integer Quadratic Programming) in the same manner as the standard optimal control problem without future disturbances. By using MLD preview control neural network approximation the control input signal within the control cycle period can be calculated, while maintaining the similar control performance of the MLD preview control.

II. FUTURE SCOPE

Future work can be done in autonomous vehicles on their road behavior and safety.[1]

A non-negligible amount of time may be needed for certain variable dampers to reach the desired stiffness and damping properties. So future work with the actuator delay can be done to modify MLD preview control to fit these response-delayed, semi-active devices.[2]

The ability of sensors sensing the road irregularities can be improved in future to increase accuracy of cloud computing technique. [3]­

Training ANN controllers can also be done to study its effect on Semi-Active suspensions with MR dampers.[7]

Conclusion

When the effective driving comfort and vehicle handling are necessary then the semi-active suspension system proves to be more efficient. As it can adopt certain modifications which will help it to achieve the high quality of vehicle stability and road comfort. By simply controlling the actuation force the damping can be adjusted efficiently.[1] The MR dampers can achieve required stiffness and damping within the short interval of time than the other ways.[2] By computing the cloud database of upcoming road-conditions the adaptive semi-active suspension systems can improve driving and confirm the safety of passengers.[3]

References

[1] Ruben Begnis, Giulio Panzani, Mirko Brentari, Luca Zaccarian, \"An LMI-based approach for the control of semi-active magnetorheological suspensions\", Volume 53, Issue 2, 2020, Pages 14363-14368, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2020.12.1389. [2] G. BEL HAJ FREJ, X. MOREAU, E. HAMROUNI, A. BENINE-NETO, V. HERNETTE, \"Multi-Modes Control for Semi-Active Suspension Systems\", Volume 53, Issue 2, 2020, Pages 14407-14412, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2020.12.1405. [3] Hakan Basargan, András Mihály, Péter Gáspár, Oliver Sename, \"Cloud-based adaptive semi-active suspension control for improving driving comfort and road holding\", Volume 55, Issue 14, 2022, Pages 89-94, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2022.07.588. [4] Kaoru Sato, Kazuhiko Hiramoto, \"Vibration control of semi-active suspension by the neural network that learned the optimal preview control of MLD model\", Volume 55, Issue 27, 2022, Pages 515-520, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2022.10.564. [5] András Mihály, Ádám Kisari, Péter Gáspár, Balázs Németh, “Adaptive Semi-Active Suspension Design Considering Cloud-based Road Information”, Volume 52, Issue 5, 2019, Pages 249-254, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2019.09.040. [6] Lin Yang, Ruochen Wang, Renkai Ding, Wei Liu, Zhihao Zhu, \"Investigation on the dynamic performance of a new semi-active hydro-pneumatic inerter-based suspension system with MPC control strategy\", Mechanical Systems and Signal Processing, Volume 154, 2021, 107569, ISSN 0888-3270, https://doi.org/10.1016/j.ymssp.2020.107569. [7] Mostafa Ghoniem, Taher Awad, Ossama Mokhiamar, \"Control of a new low-cost semi-active vehicle suspension system using artificial neural networks\", Alexandria Engineering Journal, Volume 59, Issue 5, 2020, Pages 4013-4025, ISSN 1110-0168, https://doi.org/10.1016/j.aej.2020.07.007.

Copyright

Copyright © 2024 Rushikesh R. Wagh, Dr. Pravin R. Kale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.