Active Suspension Control Based on Particle Swarm Optimization

Author(s): Shaobin Lv, Guoqiang Chen*, Jun Dai

Journal Name: Recent Patents on Mechanical Engineering

Volume 13 , Issue 1 , 2020

Become EABM
Become Reviewer


Background: The active suspension can be adjusted in real time according to the change of road condition and vehicle state to enhance the performance of active suspension that has received widespread attention. Suspension control strategies and actuators are the key issues of the active suspension, and are the main research directions for active suspension patents.

Objective: The numerical analysis method is proposed to study the performance characteristics of the active suspension controlled by different controllers.

Methods: The active suspension control model and control strategy based on particle swarm optimization are established, and two active suspensions controlled by the sliding mode controller and the fuzzy PID controller are proposed. Moreover, two active suspension systems are optimized by particle swarm optimization.

Results: The results of the analysis show that the performance of the active suspension is significantly improved compared with the passive suspension when the vehicle runs on the same road. The ride comfort of the active suspension controlled by the fuzzy PID controller has the best adaptive performance when the vehicle runs on different grade roads or white noise roads. The active suspension controlled by the fuzzy PID controller has the best ride comfort.

Conclusion: A good control strategy can effectively improve the performance of the active suspension. To improve the performance of the active suspension, it can be controlled by utilizing different control strategies. The results lay a foundation for the active suspension experiments, the dynamic analysis and the optimization design of suspension structure.

Keywords: Active suspension, fuzzy PID controller, numerical analysis method, particle swarm optimization, performance characteristic, sliding mode controller.

Zhang JH, Hong LS, Yang WZ, He ZP, Guo P. Review of technique application and performance evaluation for the vehicle suspension system. Mach Des Res 2015; 31(6): 147-53.
Coppuck F. Vehicle suspension. US20170050484 (2017)
Kou F, Du J, Wang Z, Li D, Xu J. Nonlinear modeling and coordinate optimization of a semi-active energy regenerative suspension with an electro-hydraulic actuator. Algorithms 2018; 11(12): 1-17.
Murray IG. Vehicle suspension. US20120061937 (2012)
Chen SA, Wang JC, Yao M. Design method of holographic optimal sliding mode controller for semi-active suspension of vehicle. JTTE 2016; 16(3): 72-99.
Zhao CH, Chen SA, Wang JC. Influences of stiffness and damping parameters on control of active suspension based on LQG. Trans Chin Soc Agric Mach 2015; 46(12): 301-54.
Roshan J, Shankar K. Modeling and optimization of passive and semi-active suspension systems for passenger cars to improve ride comfort and isolate engine vibration. J Vib Control 2013; 19(10): 1471-9.
Wu YW, Qiang ZQ, Wu KJ. An active suspension device and its control method. CN108058562 (2018)
Wang RC, Ye Q, Sun ZY, Zhou WQ, Chen L. Study of mode switch of the hydraulically interconnected inerter-spring-damper suspension system. J Mech Eng 2017; 53(6): 110-5.
Wang RC, Ding YS, Sun D, Ding RK, Meng XP. Dynamic performance coordination control of hydraulic electrical energy-regenerative suspension based on road excitation self-adaptation. Trans Chin Soc Agric Eng 2019; 35(6): 55-64.
Hemanth K, Kumar H, Gangadharan KV. Vertical dynamic analysis of a quarter car suspension system with MR damper. J Braz Soc Mech Sci Eng 2017; 39(1): 41-51.
Joseph HB, Damon AW, Weldon FW, Don AB, Andreas MG. Constat force suspension, near constant force suspension, and associated control algorithms. US5999868 (1999)
Hamilton KJ, Soldan D. Semi-active suspension controls. US20180022402 (2018)
Yan S, Sun W. Self-powered suspension criterion and energy regeneration implementation scheme of motor-driven active suspension. Mech Syst Signal Process 2017; 94: 297-311.
Wang G. Finiter time sliding mode tracking control for active suspension systems via extended super-twisting observer. Proc Inst Mech Eng, Part I, J Syst Control Eng 2017; 231(6): 459-70.
Kilicaslan S. Control of active suspension system considering nonlinear actuator dynamics. Nonlinear Dyn 2018; 91(2): 1383-94.
Prabu K, Jancirani J, John D, Arun B. Vibrational control of air suspension system using PID controller. J Vibroeng 2013; 15(1): 132-8.
Khan L, Qamar S, Khan U. Adaptive PID control scheme for full car suspension control. J Chin Inst Eng 2016; 39(2): 169-85.
Amer NH, Ramli R, Wan Mahadi WNL, Zainul Abidin MA, Rasol Z. Implementations of PID controller and its transient behavior in active suspension system. Adv Mater Res 2014; 895: 490-9.
Anantachaisilp P, Lin Z. Fractional order PID control of rotor suspension by active magnetic bearings. Actuators 2017; 6(4): 1-31.
Awad MN, Sokar MI, Abdrabbo SM, El-Arabi ME. Hydro-pneumatic energy harvesting suspension system using a PSO based PID controller. SAE Int J Commer Veh 2018; 11(4): 223-30.
Huang SJ, Lin CW. Application of a fuzzy enhance adaptive control on active suspension system. Int J Comput Appl Technol 2004; 20(4): 152-60.
Chiou JS, Liu MT. Using fuzzy logic controller and evolutionary genetic algorithm for automotive active suspension system. Int J Automot Technol 2009; 10(6): 703-10.
Ye XM, Long HY, Pei WC, Li YG, Zheng S. Fuzzy control for automotive semi-active suspension with MR damper. J North China Univ Sci Technol (Nat Sci Ed) 2018; 40(2): 96-9.
Senthilkumar P, Sivakumar K, Kanagarajan R, Kuberan S. Fuzzy control of active suspension system using full car model. Mechanika 2018; 24(2): 240-7.
Zhang K, Xi WH, Deng WH, Yang XJ, Gao J, Lu XJ. Fuzzy control simulation of full vehicle semi-active suspension based on car sim-simulink co-simulation. J Kunming Univ Sci Technol (Nat Sci Ed) 2015; 40(1): 39-44.
Alfadhli A, Darling J, Hillis AJ. The control of an active seat suspension using an optimized fuzzy logic controller based on preview information from a full vehicle model. Vibration 2018; 1: 20-40.
Senthil K, Vijayarangan MS. Design of LQR controller for active suspension system. Indian J Eng Mater Sci 2006; 13(3): 173-9.
Satyanarayana VSV, Sateesh B, Rao NM. Parameters optimization of vehicle suspension system for better ride comfort. IJVP 2018; 4(2): 186-99.
Hasbullah F, Faris WF. An evaluation of LQR and fuzzy logic controllers for active suspension using half car model. IJVNV 2010; 6(2): 200-14.
Zhang HT, Wang ZP, Wang YX, Wang XL. Dynamics simulation and analysis of entire-vehicle model’s active suspension with LQG controller. J Jinggangshan Univ (Nat Sci Ed) 2014; 35(4): 67-73.
Wang DZ, Zhao DX, Gong M, Yang DB. Nonlinear predictive sliding mode control for active suspension system. Shock Vib 2018; 2018: 1-10.
Alves Uiliam Nelson LT, Garcia José Paulo F, Teixeira Marcelo CM, Garcia C, Saulo FB. Sliding mode control for active suspension system with data acquisition delay. Math Probl Eng 2014; 2014: 1-13.
Chen SA, Wang JC, Yao M. Improved optimal sliding mode control for a non-linear vehicle active suspension system. J Sound Vibrat 2017; 395: 1-25.
Lou SM, Fu Z, Xu CL. Semi-active suspension control based on sliding mode theory. Automot Eng 2010; 32(5): 434-8.
Wang J, Cai YM. Parameter optimization of exponent approaching sliding mode control method for active suspension. J Chongqing Univ Technol (Nat Sci Ed) 2017; 31(9): 15-21.
Gade PV. Frequency-weighted vehicle suspension control. US20070118260 (2007)
Wang HX, Kang K, Xu YJ, Hu HP. A control method of vehicle active suspension system based on H preview control. CN107168279 (2007)
Jing H, Wang RR, Li C, Bao JD. Robust finite-frequency H control of full-car active suspension. J Sound Vibrat 2019; 441: 221-39.
Li S, Chen SF, Liu B, Li YM, Liang YS. Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks. Neurocomputing 2012; 9: 1-10.
Li H, Feng YH, Su LW. Vehicle active suspension vibration control based on robust neural network. Chin J Constr Mach 2017; 15(4): 324-37.
Zhou B, Zhao BH. Simulation study of self-adaptive Fuzzy-PID control of active suspension. J Hunan Univ Nat Sci 2009; 36(12): 27-30.
Wang RC, Ding YS, Sun D, Ding RK, Meng XP. Dynamic performance coordination control of hydraulic electrical energy-regenerative suspension based on road excitation self-adaptation. Trans Chin Soci Agric Engi 2019; 35(6): 55-64.
Yang XF, Zhao WT, Liu YL, Shen YJ, Yang Y, Liu CN. An active control method of vehicle ISD suspension based on single neuron PID control. CN109334378 (2019)
Song BK, An JH, Choi SB. A new fuzzy sliding mode controller with a disturbance estimator for robust vibration control of a semi-active vehicle suspension system. Appl Sci (Basel) 2017; 7(10): 1-20.
Pang H, Liang J, Wang JP, Liu F. Adaptive fuzzy sliding mode control for vehicle active suspension systems considering system uncertainty. J Vib Shock 2018; 37(15): 261-9.
Souilem H, Derbel N. Neuro-fuzzy control of vehicle active suspension system. Int J Circ Syst Signal Pr 2018; 12: 423-31.
Yue JZ, Ruan HB, Liu CZ, Liu W, Xiong N, Chen YX. An active suspension control system and control method based on H PID. CN106647256 (2016)
Eski I, Yildirim S. Vibration control of vehicle active suspension system using a new robust neural network control system. Simul Model Pract Theory Simul Model Pract Theory 2009; 17(5): 778-93.
Priyandoko G, Mailah M, Jamaluddin H. Vehicle active suspension system using skyhook adaptive neuro active force control. Mech Syst Signal Process 2009; 23(3): 855-68.
Chen GQ, Lv SB, Dai J. Study on PID control of vehicle semi-active suspension based on genetic algorithm. Int J Innov Comput, Inf Control 2019; 15(3): 1093-114.
Arya Y. Improvement in automatic generation control of two-area electric power systems via a new fuzzy aided optimal PIDN-FOI controller. ISA Trans 2018; 80: 475-90.
Zeng JR, Gu ZQ, Li WP, Liang XB, Peng GP. A research on the fuzzy PID control for vehicle semi-active suspension based on genetic algorithm. Automot Eng 2010; 32(5): 429-33.
Wang W, Xue YB, Song YL, Du XC. Fuzzy-PID control strategy for an active suspension based on optimal control laws with genetic algorithm. J Vib Shock 2012; 31(22): 157-62.
Huang DS, Zhang JQ, Liu YL. The PID semi-active vibration control on nonlinear suspension system with time delay. J Intell Transpor Syst Res 2018; 16(2): 125-37.
Metered H, Elsawaf A, Vampola T, Sika Z. Vibration control of MR-damped vehicle suspension system using PID controller tuned by particle swarm optimization. SAE Int J Passeng Cars Mech Syst 2015; 8(2): 426-35.
Talib MHA, Mat Darus IZ. Development of fuzzy logic controller by particle swarm optimization algorithm for semi-active suspension system using magneto-rheological damper. WSEAS Trans Syst Control 2014; 9(1): 77-85.
Das RR, Elumalai VK, Ganapathy Subramanian R, Ashok K, Kadiyam V. Adaptive predator-prey optimization for tuning of infinite horizon LQR applied to vehicle suspension system. Appl Soft Comput 2018; 72: 518-26.
Pedro JO, Dangor M, Dahunsi OA, Ali MM. Differential evolution-based PID control of nonlinear full-car electrohydraulic suspensions. Math Probl Eng 2013; 2013: 1-13.
Wei W, Song YL, Xue YB, Jin HL, Hou JC, Zhao ML. An optimal vibration control strategy for a vehicles active suspension based on improved cultural algorithm. Appl Soft Comput 2015; 28: 167-74.
Bazios P, Khoshnoud F, Esat I. Energy harvesting from suspension system and self-powered vibration control for a seven degree of freedom vehicle model. P I Mech Eng K-J Mul 2018; 232(3): 342-56.
Yang ZY, Liang S, Zhou Y, Zhao D. Sliding mode control for vibration comfort improvement of a 7-DOF nonlinear active vehicle suspension model. JRM 2019; 31(1): 95-103.
Bouazara M, Gosselin-Brisson S, Richard MJ. Design of an active suspension control for a vehicle model using a genetic algorithm. Trans Can Soc Mech Eng 2007; 31(3): 317-33.
Youn I, Wu L, Youn E, Tomizuka M. Attitude motion control of the active suspension system with tracking controller. Int J Automot Technol 2015; 16(4): 593-601.
Tharakeshwar A, Ghosal A. Modeling and simulation of a three-wheeled mobile robot on uneven terrains with two-degree-of-freedom suspension mechanisms. Mech Based Des Struct Mach 2015; 43(4): 466-86.
Bagheri A, Mahmoodabadi MJ, Rostami H, Kheybari S. Pareto optimization of a two-degree of freedom passive linear suspension using a new multi objective genetic algorithm. Int J Eng Trans A-Bas 2011; 24(3): 291-9.
Narayana S, Raju GV. Stochastic optimal control of non-stationary response of a single-degree-of-freedom vehicle model. J Sound Vibr 1990; 141(3): 449-63.
Dukkipati RV, Osman MOM, Vallurupalli SS. Real time adaptive control compared with stochastic optimal control of active suspension. Trans Can Soc Mech Eng 1993; 17(4): 713-34.
Kou FR, Xu JA, Liu DP, Zhang K, Sun K. Study on dual sliding mode control of EHA active suspensions. China Mech Eng 2019; 30(5): 542-8.
Liang J, Pang H, Wang JP, Chen JA. Design and analysis for sliding mode-following controller of semi-active suspension system. Mech Sci Technol Aerospace Eng 2017; 36(7): 1022-8.
Kou FR, Wang Z, Du JF, Li D, Xu JN, He LL. Study on force tracking control of EHA active suspensions. China Mech Eng 2017; 28(24): 2964-70.
Yu ZS. Automobile Theory. 5th ed. China Machine Press: Beijing 2016.
Nesrine T. Design of fuzzy controller rule base using bat algorithm. Energy Procedia 2019; 162: 241-50.
Arya Y. AGC of PV-thermal and hydro-thermal power systems using CES and a new multi-stage FPIDF-(1+PI) controller. Renew Energy 2019; 134: 796-806.
Zhao Q, He F, Wang X, Liu JX. Fuzzy-PID control of vehicle active suspension based on genetic algorithm optimization. J Chongqing Univ Technol (Nat Sci) 2016; 30(2): 6-11.
Arya Y. Automatic generation control of two-area electric power systems using optimized fuzzy PID with filter plus double integral controller. J Franklin Inst 2018; 355(11): 4583-617.
Sarabakha A, Fu CH, Kayacan E. Intuit before tuning: Type-1 and type-2 fuzzy logic controllers. Appl Soft Comput 2019; 81: 1-13.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Published on: 12 February, 2020
Page: [60 - 78]
Pages: 19
DOI: 10.2174/2212797612666191118123838
Price: $25

Article Metrics

PDF: 13