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Vibration control of semi-active suspension system using PID controller with advanced firefly algorithm and particle swarm optimization

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Abstract

Magnetorheological (MR) damper control for semi-active system is one of the areas of interest investigated to improve the ride comfort and stability of vehicle performance. Many types of controllers used to control the semi-active MR damper have recently been investigated by previous researchers. It is found that the improper design of control scheme has led to an unpredictable optimum target force. Therefore, this study aims to investigate an intelligent optimizer called advanced firefly algorithm (AFA) to compute the proportional-integral-derivative (PID) controller for semi-active suspension system. The performance of the PID controller with the AFA tuning was investigated and compared to the original FA technique and other conventional and intelligent optimizers as well as non-PID controller namely as heuristic method, particle swarm optimization (PSO) and Skyhook controller. A MATLAB Simulation environment was used to generate the simulation model of semi-active suspension system complete will all control elements. The study of the controllers has shown a significant improvement as the proposed PID-AFA is capable of reducing the amplitude of the sprung acceleration and body acceleration responses up to 56.5% and 67.1%, respectively compared to PID-HEURISTIC, PID-FA, PID-PSO, Skyhook and passive systems.

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References

  • Ab Talib MH, Mat Darus IZ (2014) Development of fuzzy logic controller by particle swarm optimization algorithm for semi-active suspension system using magneto-rheological damper. WSEAS Trans Syst Cont 9:77–85

    Google Scholar 

  • Ab Talib MH, Mat Darus IZ, Mohd Samin P (2019) Fuzzy logic with a novel advanced firefly algorithm and sensitivity analysis for semi-active suspension system using magneto-rheological damper. J Ambient Intell Human Comput 10:3263–3278

    Google Scholar 

  • Ali N, Othman MA, Husain MN, Misran MH (2014) A review of firefly algorithm. J Eng Applied Scie 9(10):1732–1736

    Google Scholar 

  • Al-wagih K (2015) Improved firefly algorithm for unconstrained optimization problems. Int J Comp Apps Tech Research 4(1):77–81

    Google Scholar 

  • Ang KH, Chong G, Li Y (2005) PID control system analysis, design, and technology. IEEE Trans Cont Syst and Techn 13(4):559–576

    Google Scholar 

  • Arican M, Polat K (2020) Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems. J Artif Intel Syst 2(1):27–37

    Google Scholar 

  • Asgharnia A, Jamali A, Shahnazi R, Maheri A (2020) Load mitigation of a class of 5-MW wind turbine with RBF neural network based fractional-order PID controller. ISA Trans 96(1):272–286

    Google Scholar 

  • Baraudi U, Bin-Yahya M, Alshammari M (2019) Ticket-based QoS routing optimization using genetic algorithm for WSN applications in smart grid. J Ambient Intell Human Comput 10:1325–1338

    Google Scholar 

  • Beschi M, Padula F, Visioli A (2016) Fractional robust PID control of a solar furnace. Cont Eng Practice 56:190–199

    Google Scholar 

  • Boopathi M, Abudhahir A (2015) Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems. J Eng Research 3(2):79–94

    Google Scholar 

  • Chen K, Zhou F, Liu A (2018) Chaotic dynamic weight particle swarm optimization for numerical function optimization. Knowl Based Syst 139:23–40

    Google Scholar 

  • Coelho S, Cocco V (2013) Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build 59:273–278

    Google Scholar 

  • ​Fister I, Yang XS, Brest J, Fister I Jr (2013a) Expert systems with applications modified firefly algorithm using quaternion representation. Exp Syst App 40(18):7220–7230

    Google Scholar 

  • Fister I, Fister I Jr, Yang XS, Brest J (2013b) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:1–13

    Google Scholar 

  • Greco A, Urso DD, Cannizzaro F, Pluchino A (2018) Damage identification on spatial Timoshenko arches by means of genetic algorithms. Mech Syst Signal Proc 105:51–67

    Google Scholar 

  • Hu G, Liu Q, Ding R, Li G (2017) Vibration control of semi-active suspension system with magnetorheological damper based on hyperbolic tangent model. Adv Mech Eng 9(5):1–15

    Google Scholar 

  • Jeon H, Lee C (2015) Proportional-integral-derivative control of rigid rotor-active magnetic bearing system via eigenvalue assignment for decoupled translational and conical modes. J Vib Cont 21(12):2372–2393

    Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization.technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department 200, pp 1–10

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, 27 Nov–1 Dec, Perth, Australia, pp 1942–1948

  • Khaksar M, Rezvani A, Hassan M (2018) Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller. Neu Comp App 29(3):837–853

    Google Scholar 

  • Kumar N, Prasad D, Panda G (2019) PSO based adaptive narrowband ANC algorithm without the use of synchronization signal and secondary path estimate. Mech Syst Signal Proc 114:378–398

    Google Scholar 

  • Liu X, Wang N, Wang K, Huang H, Li Z, Sarkodie-Gyan T, Li W (2019) Optimizing vibration attenuation performance of a magnetorheological damper-based semi-active seat suspension using artificial intelligence. Front Mater 6:269

    Google Scholar 

  • Liu X, Wang N, Wang K, Chen SM, Sun S, Li Z, Li W (2020) A new AI-surrogate model for dynamics analysis of a magnetorheological damper in the semi-active seat suspension. Smart Mater Struct 2020:1–15

    Google Scholar 

  • Luo J, Liu Q, Yang Y, Li X, Chen M, Cao W (2017) An artificial bee colony algorithm for multi-objective optimisation. Appl Soft Comp J 50:235–251

    Google Scholar 

  • Madić M, Marković D, Radovanović M (2013) Comparison of meta-heuristic algorithms for solving machiningg optimization problems. Mech Eng 11(1):29–44

    Google Scholar 

  • Martín-moreno R, Vega-rodríguez MA (2018) Multi-objective artificial bee colony algorithm applied to the bi-objective orienteering problem. Know-Based Syst 154(11):93–101

    Google Scholar 

  • Mohd Yatim H, Mat Darus IZ (2014) Self-tuning active vibration controller using particle swarm optimization for flexible manipulator system. WSEAS Trans Syst Cont 9:55–66

    Google Scholar 

  • Osaba E, Fernando XY, Enrique D, Masegosa AD, Perallos A (2017) A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Comp 21(18):5295–5308

    Google Scholar 

  • Pal SK, Rai CS, Singh AP (2012) Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. Int J Intel Syst Appl 4(10):50–57

    Google Scholar 

  • Pinto BQ, Ribeiro CC, Rosseti I, Noronha TF (2020) A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model. J Glob Opt 2020:1–25

    MathSciNet  MATH  Google Scholar 

  • Qiao D, Mu N, Liao X, Le J, Yang F (2020) Improved evolutionary algorithm and its application in PID controller optimization. Inf Sci 63(199205):1–199205

    Google Scholar 

  • Qin Y, Langari R, Wang Z, Xiang C, Dong M (2017) Road excitation classification for semi-active suspension system with deep neural networks. J Intel Fuzzy Syst 33(3):1907–1918

    Google Scholar 

  • Rao KD (2014). Modeling, simulation and control of semi active suspension system for automobiles under MATLAB simulink using PID Controller. In: Third international conference on advances in control and optimization of dynamical system, 13–15 March, Kampur, India, pp 827–831

  • Rashid MM, Rahim NA, Hussain MA, Rahman MA (2011) Analysis and experimental study of magnetorheological-based damper for semiactive suspension system using fuzzy hybrids. IEEE Trans Ind App 47(2):1051–1059

    Google Scholar 

  • Riahi A, Balochian S (2012) Control design of a semi active suspension using optimal, PID and sliding mode theory. Acta Electr 53(2):144–147

    Google Scholar 

  • Sundari MG, Rajaram M, Balaraman S (2016) Application of improved firefly algorithm for programmed PWM in multilevel inverter with adjustable DC sources. Appl Soft Comp J 41:169–179

    Google Scholar 

  • Taskin Y, Haciaglu Y, Yagiz N (2017) Experimental evaluation of a fuzzy logic controller on a quarter car test rig. J Braz Soc Mech Sci Eng 39(7):2433–2445

    Google Scholar 

  • Tighzert L, Fonlupt C, Mendil B (2017) A set of new compact firefly algorithms. Swarm Evol Comp 11:1–24

    Google Scholar 

  • Tilahun SL, Ong HC (2012) Modified firefly algorithm. J Appl Math. https://doi.org/10.1155/2012/467631

    Article  MathSciNet  MATH  Google Scholar 

  • Tsai HC (2020) Artificial bee colony directive for continuous optimization. Appl Soft Comp 87:105982

    Google Scholar 

  • Uthayakumar J, Metawa N, Shankar K, Lakshmanaprabu SK (2020) Financial crisis prediction model using ant colony optimization. Int J Inf Manag 50:538–556

    Google Scholar 

  • Vaijayanti B (2017) Robotic arm control using PID controller and inverse kinematics. Int J Eng Dev Res 5(2):1571–1579

    Google Scholar 

  • Vranc D, Peng Y, Strmcnik S (1999) A new PID controller tuning method based on multiple integrations. Cont Eng Prac 7:623–633

    Google Scholar 

  • Wang J, Zhu Y, Qi R, Zheng X, Li W (2020) Adaptive PID control of multi-DOF industrial robot based on neural network. J Ambient Intell Human Comput 2020:1–12

    Google Scholar 

  • Yang X (2010) Nature-inspired metaheuristic algorithms (2nd ed.) United Kingdom

  • Yang J, Sun S, Guo N, Ning D, Nakano M, Li Z, Li WH (2020) Development of a smart rubber joint for train using shear thickening fluids. Smart Mater Struct 29(5):055036

    Google Scholar 

  • Yao J, Shi W, Zheng J, Zhou H (2013) Development of a sliding mode controller for semi-active vehicle suspensions. J Vib Cont 19(8):1152–1160

    Google Scholar 

  • Zou Z, Qian Y (2019) Wireless sensor network routing method based on improved ant colony algorithm. J Ambient Intell Human Comput 10:991–998

    Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to Minister of Education Malaysia (MOE) and Universiti Teknologi Malaysia (UTM) for funding and providing facilities to conduct this research. This research is supported by UTM Research University grant, Vote No. 17J49.

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Authors and Affiliations

  1. School of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, 81310, Malaysia

    Mat Hussin Ab Talib, Intan Zaurah Mat Darus, Pakharuddin Mohd Samin, Hanim Mohd Yatim, Mohd Ibthisham Ardani & Nik Mohd Ridzuan Shaharuddin

  2. Faculty of Mechanical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, 40450, Malaysia

    Muhamad Sukri Hadi

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  1. Mat Hussin Ab Talib
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  2. Intan Zaurah Mat Darus
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Correspondence to Mat Hussin Ab Talib.

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Ab Talib, M.H., Mat Darus, I.Z., Mohd Samin, P. et al. Vibration control of semi-active suspension system using PID controller with advanced firefly algorithm and particle swarm optimization. J Ambient Intell Human Comput 12, 1119–1137 (2021). https://doi.org/10.1007/s12652-020-02158-w

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