Open Access Open Access  Restricted Access Subscription or Fee Access

A Comparative Study of Particle Swarm Optimized Control Techniques for Active Suspension System

(*) Corresponding author

Authors' affiliations



This work presents a comparative study for performance evaluation of quarter vehicle Active Suspension System (ASS) using different control techniques. Linear and non-linear mathematical models of the systems are presented and controlled using Proportional Integral Derivative (PID), Feedback Linearization, and Fuzzy Logic Control (FLC). Particle Swarm Optimization (PSO) technique is applied to optimize the gains of the implemented controllers. A simulation process is done using MATLAB/SIMULINK and the results show that the proposed PSO-optimized PID controller has enhanced the root mean square (r.m.s) values of ride comfort, load carrying, and road holding of the linear suspension system by 26.82%, 20.95%, and 22.72% respectively compared to the linear passive suspension system. Whereas, for the nonlinear suspension system, the PSO-optimized FLC has provided better compromise of the performance criteria. The controller has improved the r.m.s values of ride comfort, load carrying, and road handling by 40.61%, 44.21%, and 27.46% respectively compared to the non-linear passive suspension system.
Copyright © 2022 Praise Worthy Prize - All rights reserved.


Active Suspension; Feedback Linearization; Fuzzy Logic Control; Particle Swarm Optimization; PID; Quarter-Vehicle Model

Full Text:



D. Bastrow, G. Howard, and J. P. Whitehead, Car Suspension and Handling, 4th ed., SAE International, Pennsylvania (2004).

M. Issa, and A. Samn, Passive vehicle suspension system optimization using Harris Hawk Optimization algorithm, Mathematics and Computers in Simulation, Vol. 191 (328-345), 2022.

B. Zhang, C. A, Tan. And T. Dai, Ride comfort and energy dissipation of vehicle suspension system under non-stationary random road excitation, Journal of Sound and Vibration, Vol. 511 (116347), 2021.

Mazouzi, A., Yssaad, S., Karas, I., A Hybrid Method for Road Marking Detection, (2022) International Review of Automatic Control (IREACO), 15 (1), pp. 38-43.

Yerrawar, R., Arakerimath, R., Taguchi Based Grey Relational Analysis Methodology for Semi Active Suspension System Using MR Damper, (2017) International Review of Mechanical Engineering (IREME), 11 (9), pp. 666-672.

C. Smith, B. Hill, G. Wheatley, R. M. Nejad, and N. Sina, Fatigue reliability assessment of the new design of rear suspension system of the JCU motorsport car, Structures, Vol. 36 (473-481), 2022.

M. Appleyard, and P. E. Wellsted, Active Suspensions: some background, IEE Proc. - Control Theory and Appl., 142 (2), 1995.

M. Zhang, X. Jing, W. Huang, and P. Li, Saturated PD-SMC method for suspension systems by exploiting beneficial nonlinearities for improved vibration reduction and energy-saving performance, Mechanical Systems and Signal Processing, Vol. 179 (109376), 2022.

D A Patriawan, H Irawan, A Noerpamoengkas, B Setyono, and A Y Ismail, Definition, criteria and approaches in designing suspension system with active controls, IOP Conference Series: Materials Science and Engineering, 1010, 012006, 2021.

Bataineh, A., Batayneh, W., Okour, M., Intelligent Control Strategies for Three Degree of Freedom Active Suspension System, (2021) International Review of Automatic Control (IREACO), 14 (1), pp. 17-27.

M. Dassisti, A.G. Olabi, and G. Brunetti, Application of Magnetorheological Fluids (MRF) in a Suspension System, Encyclopedia of Smart Materials, Vol. 5 (269-283), 2022.

Prabu, K., Jancirani, J., John, D., Dynamic Characteristic Analysis on Half Car Electro Pneumatic Suspension System, (2013) International Review of Mechanical Engineering (IREME), 7 (3), pp. 436-441.

R. Zhang, L. Zhao, X. Qiu, H. Zhang, and X. Wang, A comprehensive comparison of the vehicle vibration energy harvesting abilities of the regenerative shock absorbers predicted by the quarter, half and full vehicle suspension system models, Applied Energy, Vol. 272 (115180), 2020.

El Majdoub, K., Ouadi, H., Touati, A., LQR Control for Semi-Active Quarter Vehicle Suspension with Magnetorhehological Damper and Bouc-Wen Model, (2014) International Review on Modelling and Simulations (IREMOS), 7 (4), pp. 703-711.

Amer, N., Ramli, R., Wan Mahadi, W., Zainul Abidin, M., Isa, H., Foong, S., Implementation of LQR Controller on Electromagnetic Suspension System for Passenger's Car, (2014) International Review of Mechanical Engineering (IREME), 8 (1), pp. 258-264.

M. Haddar., R. Chaari., S.C. Baslamisli, and F. Chaari, Intelligent PD controller design for active suspension system based on robust model-free control strategy, Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 233(14), 4863-4880, 2019.

S. Marcu, D. Popa, N.D. Stănescu, and N. Pandrea, Design of the optimal control for the active suspension, Scientific Bulletin - Automotive Series, 31, 2021.

V. K. Maurya and N. S. Bhangal, Optimal Control of Vehicle Active Suspension System, Journal of Automation and Control Engineering, Vol. 6, (No. 1), pp. 22-26, 2018.

Gaya, M., Bature, A., Yusuf, L., Madugu, I., Abubakar, U., Abubakar, S., Comparison of Control Strategies Applied to Nonlinear Quarterly Car Passive Suspension System, (2015) International Review of Automatic Control (IREACO), 8 (3), pp. 203-208.

Khodja, M., Larbes, C., Ramzan, N., Ibrahim, A., Implementation of Heuristical PID Tuning for Nonlinear System Control, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 108-114.

Oultiligh, A., Ayad, H., El Kari, A., Mjahed, M., El Gmili, N., A Hybrid PSO-GWO Algorithm for Robot Path Planning in Uncertain Environments, (2021) International Review of Automatic Control (IREACO), 14 (6), pp. 360-372.

Y. Zhang, Y. Liu, Z. Wang, R. Bai, and L. Liu, Neural networks-based adaptive dynamic surface control for vehicle active suspension systems with time-varying displacement constraints, Neurocomputing, Vol. 408 (176-187), 2020.

L. Liu, X. Li, Y.J. Liu, and S. Tong, Neural network based adaptive event trigger control for a class of electromagnetic suspension systems, Control Engineering Practice, Vol. 106 (104675), 2021.

Reddipogu, J., Elumalai, V., Multi-Objective Model Predictive Control for Vehicle Active Suspension System, (2020) International Review of Automatic Control (IREACO), 13 (5), pp. 255-263.

Fas, M., Benrabah, M., Guessoum, A., Robust Adaptive Generalized Predictive Control Based on Takagi-Sugeno Fuzzy Model, (2021) International Review of Automatic Control (IREACO), 14 (3), pp. 135-143.

I. Kioutsoukis, M. Foo, and A. H. Tan, Application of Multi-Input Uncorrelated Periodic Signals for Identification of Active Suspension System, IFAC-PapersOnLine, Vol. 55 (Issue: 1), 2022.

J. Mrazgua, R. Chaibi, E.H. Tissir, and M. Ouahi, Static output feedback stabilization of T-S fuzzy active suspension systems, Journal of Terramechanics, Vol. 97 (19-27), 2021.

L. Ovalle, H. Ríos, and H. Ahmed, Robust Control for an Active Suspension System via Continuous Sliding-Mode Controllers, Engineering Science and Technology, an International Journal, Vol. 28 (101026), 2022.

Essam Harby, M., Elzoghby, H., Elmasry, S., Elsamahy, A., Bidirectional Control of Electric Vehicles Based on Artificial Neural Network Considering Owners Convenience and Microgrid Stability, (2020) International Review of Automatic Control (IREACO), 13 (6), pp. 304-312.

Khouili, D., Labbadi, M., Ramzi, M., Lahlouh, I., Design of a Robust Nonlinear PID Controller: Simulation and Experimental Validation for a Computer Aided Aerothermic System, (2022) International Review of Automatic Control (IREACO), 15 (1), pp. 12-19.

Ali, H., Mhmood, A., Nonlinear H-Infinity Model Reference Controller Design, (2021) International Review of Automatic Control (IREACO), 14 (1), pp. 39-50.

Zhang, S., Sakulyeva, T., Pitukhin, E., Doguchaeva, S., Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Artificial Intelligence, (2020) International Review of Automatic Control (IREACO), 13 (4), pp. 191-199.

Eid, A., Performance Improvement of Active Distribution Systems Using Adaptive and Exponential PSO Algorithms, (2021) International Review of Electrical Engineering (IREE), 16 (2), pp. 147-157.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize