Vibrations control of railway vehicles using decentralized proportional integral derivative controller with flow direction optimization algorithm

Authors

  • Nitish Department of Instrumentation and Control of Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India. Phone: +01815037681-2912
  • Amit Kumar Department of Instrumentation and Control of Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India. Phone: +01815037681-2912

DOI:

https://doi.org/10.15282/jmes.17.3.2023.9.0763

Keywords:

Active suspension system , Electro-hydraulic actuator, Flow direction algorithm, Multi-loop control structure, Proportional integral derivative controller, Power spectral densities

Abstract

The reduction of vibration-induced discomfort in vehicles is an important goal in the field of transportation engineering. Several mathematical models with various controlling techniques, from classical to modern, have been employed to achieve better ride comfort. Still, no comprehensive solution has yet been found. Therefore, this paper proposes a 17-degree-of-freedom (minimum number of coordinates) dynamic model of a full-scale railway vehicle integrated with wheel-rail contact forces and an active suspension system. Two controllers, termed system and force tracking controllers, suppress the vehicle body's vibrations. Based on a multi-loop control structure, three optimally tuned Proportional Integral Derivative controllers evaluate the desired control forces and performs the system controller’s action. While the force-tracking controller generates the command voltage to track that forces. The parameters of controllers are tuned with a novel metaheuristic optimization algorithm known as the flow direction algorithm (FDA), and the results are compared with two other optimization techniques, i.e., particle swarm optimization and ant colony optimization. The simulated results show that the ride comfort of the vehicle is improved with FDA, as the root mean square values of the lateral, roll, and yaw accelerations are reduced by 42.01%, 33.12%, and 48.24%, respectively. Moreover, the simulated results of the proposed model are validated with the experimental results of accelerations. The simulated results show that the proposed system tuned with the metaheuristic algorithm outperforms with a significant reduction in vehicle vibrations.

References

M. A. Karkoub and M. Zribi, “Active/semi-active suspension control using magnetorheological actuators,”International Journal of System Science, vol. 37, no. 1, pp. 35–44, 2006.

L. R. Miller, “Tuning passive, semi-active, and fully active suspension systems,” in Proceedings of the 27thIEEE Conference on Decision and Control, vol. 3, pp. 2047-2053, 1988.

J. Rabinow, “The magnetic fluid clutch,” Transactions of American Institute of Electrical Engineering, vol. 67, pp. 1308–1315, 1948.

W. Winslow, “Method and means for translating electrical impulses into mechanical force,” US Patent, 2417850, 1947, [Online]. Available: http://www.freepatentsonline.com/2417850.html

D. H. Wang and W. H. Liao, “Semi-active suspension systems for railway vehicles using magnetorheological dampers. Part I: System integration and modelling,” Vehicle System. Dynamics, vol. 47, no. 11, pp. 1305–1325, 2009.

X. Wei, M. Zhu, and L. Jia, “A semi-active control suspension system for railway vehicles with magnetorheological fluid dampers,” Vehicle System Dynamics, vol. 54, no. 7, pp. 982–1003, 2016.

L. H. Zong, X. L. Gong, S. H. Xuan, and C. Y. Guo, “Semi-active H∞ control of high-speed railway vehicle suspension with magnetorheological dampers,” Vehicle System Dynamics, vol. 51, no. 5, pp. 600–626, 2013.

X. Wu and M. J. Griffin, “A semi-active control policy to reduce the occurrence and severity of end-stop impacts in a suspension seat with an electrorheological fluid damper,” Journal of Sound and Vibration, vol. 203, no. 5, pp. 781–793, 1997.

S. B. Choi, J. H. Choi, M. H. Nam, C. C. Cheong, and H. G. Lee, “A semi-active suspension using ER fluids for a commercial vehicle seat,” Journal of Intelligent Material Systems and Structure, vol. 9, no. 8, pp. 601–606, 1998.

N. D. Sims and R. Stanway, “Semi-active vehicle suspension using smart fluid dampers: A modelling and control study,” International Journal of Vehicle Design, vol. 33, no. 1–3, pp. 76–102, 2003.

D. H. Wang and W. H. Liao, “Semi-active suspension systems for railway vehicles using magnetorheological dampers. Part II: Simulation and analysis,” Vehicle System Dynamics, vol. 47, no. 12, pp. 1439–1471, 2009.

B. Fu, E. Di Gialleonardo, B. Liu,and S. Bruni, “Modelling, hardware-in-the-loop tests and numerical simulation of magneto-rheological semi-active primary suspensions in a railway vehicle,” Vehicle System Dynamics, 2023.

M. M. ElMadany and M. E. Samaha, “On the optimum ride control of a stochastic model of a tractor-semitrailer vehicle,” Jounal of Sound and Vibration, vol. 156, no. 2, pp. 269–281, 1992.

B. Fu, R. L. Giossi, R. Persson, S. Stichel, S. Bruni, and R. Goodall, “Active suspension in railway vehicles: A literature survey,” Railway Engineering Science, vol. 28, no. 1, pp. 3–35, 2020.

T. Yoshimura, K. Edokoro, and N. Ananthanarayana, “An active suspension model for rail/vehicle systems with preview and stochastic optimal control,” Journal of Sound and Vibration, vol. 166, no. 3. pp. 507–519, 1993.

D. Hrovat, “Optimal active suspension structures for quarter-car vehicle models,” Automatica, vol. 26, no. 5, pp. 845–860, 1990.

M. Metin and R. Guclu, “Rail vehicle vibrations control using parameters adaptive PID controller,” Mathamatical Problems in Engineeing, vol. 2014, pp. 1-10, 2014.

I. Afolabi Daniyan and K. Mpofu, “Vibration analysis and control in the rail car system using PID controls,” Noise and Vibration Control -From Theory to Practice, IntechOpen, pp. 1–17, 2019.

M. Metin and R. Guclu, “Active vibration control with comparative algorithms of half rail vehicle model under various track irregularities,” Journal of Vibration and Control, vol. 17, no. 10, pp. 1525–1539, 2011.

I. A. Daniyan, K. Mpofu, and D. F. Osadare, “Design and simulation of a controller for an active suspension system of a rail car,” Cogent Engineering, vol. 5, no. 1, pp. 1–15, 2018.

I. A. Daniyan, K. Mpofu, O. L. Daniyan, and A. O. Adeodu, “Dynamic modelling and simulation of rail car suspension systems using classic controls,” Cogent Engineering, vol. 6, no. 1, pp. 1–20, 2019.

Nitish and A. Kumar Singh, “Active control of railway vehicle suspension using PID controller with pole placement technique,” Materials Today: Proceedings, vol. 80, pp. 278–284, 2023.

S. Sezer and A. E. Atalay, “Dynamic modeling and fuzzy logic control of vibrations of a railway vehicle for different track irregularities,” Simulatiomn Modeling. Practice and Theory, vol. 19, no. 9, pp. 1873–1894, 2011.

R. Kalaivani, P. Lakshmi, and K. Sudhagar, “Vibration control of vehicle active suspension system using novel fuzzy logic controller,” International Journal of Enterprise. Network Management, vol. 6, no. 2, pp. 139–152, 2014.

J. He, Z. Liu, and C. Zhang, “Sliding mode control of lateral semi-active suspension of high-speed train,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 24, no. 7, pp. 925–933, 2020.

S. B. Choi, Y. T. Choi, and D. W. Park, “A sliding mode control of a full-car electrorheological suspension system via hardware in-the-loop simulation,” Journal of Dynamic. Systems. Measurement and Control, Transactions of ASME, vol. 122, no. 1, pp. 114–121, 2000.

S. D. Nguyen and Q. H. Nguyen, “Design of active suspension controller for train cars based on sliding mode control, uncertainty observer and neuro-fuzzy system,” Journal of Vibration and Control, vol. 23, no. 8, pp. 1334–1353, 2017.

P. E. Orukpe, X. Zheng, I. M. Jaimoukha, A. C. Zolotas, and R. M. Goodall, “Model predictive control based on mixed H2/H∞ control approach for active vibration control of railway vehicles,” Vehicle System Dynamics, vol. 46, no. .1, pp. 151–160, 2008.

P. E. Orukpe, “Model predictive control application to flexible-bodied railway vehicles for vibration suppression,” International Journal of Engineering Research in Africa, vol. 10, pp. 25–35, 2013.

M. Olivier and J. W.Sohn, “Design optimization and performance evaluation of hybrid type magnetorheological damper,” Journal of Mechanical Science and Technology, vol. 35, no. 8, pp. 3549–3558, 2021.

M. Graa, M. Nejlaoui, A. Houidi, Z. Affi, and L. Romdhane, “Modeling and control of rail vehicle suspensions: A comparative study based on the passenger comfort,” Proceeding of the Institution of Mechanical Engineering, Part C: Journal of Mechanical Engineering Science, vol. 232, no. 2, pp. 260–274, 2018.

H. Molatefi, P. Ayoubi, and H. Mozafari, “Active vibration control of a railway vehicle carbody using piezoelectric elements,” Chinese Journal of Mechanical Engineering (English Edition), vol. 30, no. 4, pp. 963–972, 2017.

D. Hrovat, “Applications of optimal control to advanced automotive suspension design,” Journal of Dynamic System Measurement and Control, Transactions of ASME, vol. 115, no. 2B, pp. 328–342, 1993.

Y. Shen, M. Jia, X. Yang, Y. Liu, and L. Chen, “Vibration suppression using a mechatronic PDD-ISD-combined vehicle suspension system,” International Journal of Mechanical Sciences, vol. 250, p. 108277, 2023.

T. Li, Y. He, N. Wang, J. Feng, W. Gui, and K. Zhao, “Active noise cancellation of rail vehicles based on a convolutional fuzzy neural network prediction approach,” IEEE Conference on Vehicle Power and Propulsion, Gijon, Spain, 2021.

I. Eski and Ş. Yildirim, “Vibration control of vehicle active suspension system using a new robust neural network control system,” Simulation Modeling Practice and Theory, vol. 17, no. 5, pp. 778–793, 2009.

V. S. Atray and P. N. Roschke, “Neuro-fuzzy control of railcar vibrations using semiactive dampers,” Computer-aided Civil and Infrastruct Engineering, vol. 19, no. 2, pp. 81–92, 2004.

K. J. Åström and T. Hägglund, “Revisiting the Ziegler-Nichols step response method for PID control,” Journal of Process Control, vol. 14, no. 6, pp. 635–650, 2004.

G. H. Cohenn and G. A. Coon, “Theoretical consideration of retarted control” Transactions of ASME, vol. 75, pp 827-834, 1953.

A. E. Eiben, “Genetic algorithms + data structures = evolution programs,” Artificial Intelligence in Medicine, vol. 9, no. 3. pp. 283–286, 1997.

A. Slowik, “Particle swarm optimization," The Industrial Electronic Handbook, Routledge Handbooks Online, 2011.

S. Mirjalili, “Ant colony optimisation,” Studies in Computational. Intelligence, vol. 780, pp. 33–42, 2019.[43]S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, 2014.

S. L. Tilahun and H. C. Ong, “Modified firefly algorithm,” Journal of Applied Mathematics, vol. 2012, pp. 1 -12, 2012.

X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” World Congress on Nature and Biological Inspired Computing (NABIC), Coimbatore, India, 2009.

M. A. Al-Betar, M. A. Awadallah, I. Abu Doush, A. I. Hammouri, M. Mafarja, and Z. A. A. Alyasseri, “Island flower pollination algorithm for global optimization,” Journal of Supercomputing, vol. 75, no. 8, pp. 5280–5323, 2019.

A. T. El-Deen, A. A. Hakim Mahmoud, and A. R. El-Sawi, “Optimal PID tuning for DC motor speed controller based on genetic algorithm,” International Review of Automatic Control, vol. 8, no. 1, pp. 80–85, 2015.

N. F. Mohammed, E. Song, X. Ma, and Q. Hayat, “Tuning of PID controller of synchronous generators using genetic algorithm,” IEEE International Conference on Mechatronics and Automation, Tianjin, China, 2014.

A. G. Suri Babu and B. T. Chiranjeevi, “Implementation of fractional order PID controller for an AVR system using GA and ACO optimization techniques,” IFAC-PapersOnLine, vol. 49, no. 1, pp. 456-461, 2016.

D. H. Kim and J. Park, “Intelligent PID controller tuning of AVR system using GA and PSO,” Lecture Notes on Computational. Science, vol. 3645, pp. 366–375, 2005.

S. A. Adubi and S. Misra, “A comparative study on the ant colony optimization algorithms,” 11th International Conference on Electronic. Computer and Computation (ICECCO), Abuja, Nigeria, 2014.

R. J. Rajesh and C. M. Ananda, “PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal,” IEEE International Conference on Power and Advanced Control Engineering,Bengaluru, India, 2015.

S. B. Joseph, E. Dada, “Proportional-Intergral-Derivative Controller. tuning for an Inverted. Pendulum using particle swarm optimization algorithm,” FUDMA Journal of Science, vol. 2, no. 2, pp. 72–78, 2018.

M. Alamdar Ravari and M. Yaghoobi, “Optimum design of fractional order PID controller using chaotic firefly algorithms for a control CSTR system,” Asian Journal of Control, vol. 21,no. 5, pp. 2245–2255, 2019.

M. I. Mosaad, M. Osama abed el-Raouf, M. A. Al-Ahmar, and F. A. Banakher, “Maximum power point tracking of PV system based cuckoo search algorithm; review and comparison,” Energy Procedia, vol. 162, pp. 117–126, 2019.

M. Peram, S. Mishra, M. Vemulapaty, B. Verma, and P. K. Padhy, “Optimal PI-PD and I-PD controller design using cuckoo search algorithm,” 5th International Conference on Signal Processing and Integrated Networks, Noida, India, 2018.

A. Sikander, P. Verma, N. Patel, and N. K. C. Nair, “Design of controller using reduced order modeling for LED driver circuit,”. IEEE Innovative Smart Grid Technology. -Asia (ISGT-Asia), Auckland, New Zealand, 2018.

S. X. Li and J. S. Wang, “Dynamic modeling of steam condenser and design of pi controller based on grey wolf optimizer,” Mathematical Problems in Engineering., vol. 2015, pp. pp. 1 -9, 2015.

S. Yadav, S. K. Verma, and S. K. Nagar, “Optimized PID controller for magnetic levitation system,” IFAC-PapersOnLine, vol. 49, no. 1, pp. 778–782, 2016.

L. Abualigah, K. H. Almotairi, M. A. Elaziz, M. Shehab, and M. Altalhi, “Enhanced flow direction arithmetic optimization algorithm for mathematical optimization problems with applications of data clustering,” Engineering Analysis with Boundary Elements, vol. 138, pp. 13–29, 2022.

S. Pati, T. Kumar Sharma, K. Kumar Goyal, and O. Prakash Verma, “Renewable integration and energy reduction in multiple stage evaporator,” Materials Today Proceedings, vol. 80, pp. 24-31, 2022.

Y. Luo, H. Liu, L. Jia, and W. Cai, “A practical guideline to control structure selection for MIMO processes,” IEEE International Conference on Automated Logistical, Chongqing, China, 2011.

M. J. Lengare, R. H. Chile, and L. M. Waghmare, “Design of decentralized controllers for MIMO processes,” Computers. and Electrical Engineering, vol. 38, no. 1, pp. 140–147, 2012.

Y. Lei and D. T. Wu, “A new decentralized control approach for the benchmark problem,” Procedia Engineering, vol. 14, pp. 1229–1236, 2011.

S. D. Singh, R. Mathur, and R. K. Srivastava, “Dynamic response of Linke Hofmann Busch (LHB) rail coach considering suspended equipments,” Indian Journal of Science and. Technology, vol. 10, no. 38, pp. 1–20, 2017.

M. J. Goodwin, “Dynamics of railway vehicle systems,” Journal of Mechanical Working Technology, vol. 14, no. 2, pp. 245–247, 1987.

M. A. A. Abdelkareem et al., “Vibration energy harvesting in automotive suspension system: A detailed review,” Applied Energy, vol. 229, pp. 672–699, 2018.

C. Williamson, S. Lee, and M. Ivantysynova, “Active vibration damping for an off-road vehicle with displacement controlled actuators,” International Journal of Fluid Power, vol. 10, no. 3, pp. 5–16, 2009.

F. S. Shie, M. Y. Chen, and Y. S. Liu, “Prediction of corporate financial distress: An application of the America banking industry,” Neural Computing and Applications, vol. 21, no. 7, pp. 1687–1696, 2012.

M. Hajihassani, D. Jahed Armaghani, and R. Kalatehjari, “Applications of particle swarm optimization in geotechnical engineering: A comprehensive review,” Geotechnical and Geological Engineering, vol. 36, no. 2, pp. 705–722, 2018.

S. Ganguly, N. C. Sahoo, and D. Das, “A novel multi-objective PSO for electrical distribution system planning incorporating distributed generation,” Energy Systems, vol. 1, no. 3, pp. 291–337, 2010.

W. Der Chang and C. Y. Chen, “PID controller design for MIMO processes using improved particle swarm optimization,” Circuits, Systems and Signal Processing, vol. 33, no. 5, pp. 1473–1490, 2014.

T. K. Priyambodo, A. E. Putra, and A. Dharmawan, “Optimizing control based on ant colony logic for Quadrotor stabilization,” IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), Bali, Indonesia, 2016.

M. Aabid, A. Elakkary, and N. Sefiani, “PID parameters optimization using ant-colony algorithm for human heart control,” 23rdInternational Conference on Automation and Computing (ICAC), Huddersfield, United Kingdom, 2017.

B. Dhanasekaran, S. Siddhan, and J. Kaliannan, “Ant colony optimization technique tuned controller for frequency regulation of single area nuclear power generating system,” Microprocessors and Microsystems, vol. 73,p. 102953, 2020.

S. A. Dahmane, A. Azzedine, and A. Megueni, “Ant colony optimization algorithm based on optimal PID parameters for a robotic arm,”International Journal of Control Syatems and Robotics,vol. 5, pp. 8–13, 2020.

T. K. Priyambodo, A. Dharmawan, O. A. Dhewa, and N. A. S. Putro, “Optimizing control based on fine tune PID using ant colony logic for vertical moving control of UAV system,” Advances of Sciences and Technology for Society, vol. 1755, pp. 1 -6, 2016.

H. Karami, M. V. Anaraki, S. Farzin, and S. Mirjalili, “Flow Direction Algorithm (FDA): A novel optimization approach for solving optimization problems,” Computers and Industrial Engineering, vol. 156, p. 107224, 2021.

S. Singh and A. Kumar, “Modelling and analysis ofa passenger train for enhancing the ride performance using MR-based semi-active suspension,” Journal of Vibration Engineering and Technology, vol. 10, no. 5, pp. 1737–1751, 2022.

H. Cao, G. Li, and N. Liang, “Active vibration control of railway vehicle car body by secondary suspension actuators and piezoelectric actuators,” IEEE Access, vol. 10, pp. 105404–105411, 2022.

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Published

2023-09-27

How to Cite

[1]
N. Kumar and A. Kumar, “Vibrations control of railway vehicles using decentralized proportional integral derivative controller with flow direction optimization algorithm”, J. Mech. Eng. Sci., pp. 9637–9655, Sep. 2023.

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