Optimal Tuning of a LQR Controlled Active Quarter Car System Using Global Best Inertia Weight Modified Particle Swarm Optimization Algorithm

Authors

  • Oghenenyoreme Emakpo Agbroko Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Ondo State, Nigeria
  • Erastus Olarewaju Ogunti Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Ondo State, Nigeria

DOI:

https://doi.org/10.38032/jea.2023.04.002

Keywords:

Quarter Car, Linear Quadratic Regulator (LQR), Road Profile, Global Best Inertia Weight Modified Particle Swarm Optimization (GBbest-IWM-PSO) Algorithm

Abstract

A key factor in the design of a car is the comfort and safety of its passengers. The quarter-car suspension system is a feature of the car that ensures load-carrying capacity as well as comfort and safety. It comprises links, springs, and shock absorbers (dampers). Due to its significance, several research has been conducted, to increase its road handling and holding capability while trying to keep its cost moderate. To enhance customer comfort and load carrying, the road holding capacity of an active quarter car suspension was improved/controlled in this study, using the Global Best Inertia Weight Modified Particle Swarm Optimization Algorithm. The observation of the closed loop and open loop systems after designing and simulating on MATLAB reveals a significant improvement in the closed loop system's road holding ability compared to the open loop, in that, when the system was subjected to pothole, the deflection of sprung mass reached steady state in 37.37 seconds as opposed to 7000 seconds for the open loop.

References

Kashem, S., Nagarajah, R. and Ektesabi, M., 2018. Vehicle suspension systems and electromagnetic dampers. 1st ed. Singapore: Springer Nature, ISBN 9789811054785, pp. 1-175. DOI: https://doi.org/10.1007/978-981-10-5478-5

Anderson, B. and Moore, J., 1989. Optimal control: linear quadratic methods, Sydney: Prentice-Hall, ISBN 0136386512, pp. 1-374.

Alias, N.A., 2013. Linear Quadratic Regulator (LQR) controller design for Inverted Pendulum (Doctoral dissertation, Universiti Tun Hussein Malaysia). pp. 1-90.

Savaresi, S.M., Poussot-Vassal, C., Spelta, C., Sename, O. and Dugard, L., 2010. Semi-active suspension control design for vehicles. Elsevier, pp. 1-192. DOI: https://doi.org/10.1016/B978-0-08-096678-6.00001-8

Önen, Ü., Çakan, A. and İlhan, İ., 2017. Particle swarm optimization based lqr control of an inverted pendulum. Engineering and Technology Journal, 2(5), pp.168-174. DOI: https://doi.org/10.18535/etj/v2i5.01

Al-Mahturi, A. and Wahid, H., 2017. Optimal tuning of linear quadratic regulator controller using a particle swarm optimization for a two-rotor aerodynamical system. International Journal of Electronics and Communication Engineering, 11(2), pp.196-202.

Ruderman, M., Krettek, J., Hoffmann, F. and Bertram, T., 2008. Optimal state space control of DC motor. IFAC Proceedings Volumes, 41(2), pp.5796-5801. DOI: https://doi.org/10.3182/20080706-5-KR-1001.00977

Szabolcsi, R., 2018. Design and development of the LQR optimal controller for the unmanned aerial vehicle. Review of the Air Force Academy, (1), pp.45-54. DOI: https://doi.org/10.19062/1842-9238.2018.16.1.7

Burns, R., 2001. Advanced control Engineering. Oxford: Butterworth Heinemann, Jordan Hill, ISBN 0750651008, pp. 272-299.

Hassani, K. and Lee, W.S., 2014, May. Optimal tuning of linear quadratic regulators using quantum particle swarm optimization. In Proceedings of the International Conference on Control, Dynamic Systems, and Robotics (CDSR’14) (Vol. 14, p. 15).

Sen, M.A. and Kalyoncu, M., 2016. Optimal tuning of a LQR controller for an inverted pendulum using the Bees algorithm. J Autom Control Eng, 4(5), pp. 384-387 DOI: https://doi.org/10.18178/joace.4.5.384-387

Hao, Z.F., Guo, G.H. and Huang, H., 2007, August. A particle swarm optimization algorithm with differential evolution. In 2007 international conference on machine learning and cybernetics (Vol. 2, pp. 1031-1035). IEEE. DOI: https://doi.org/10.1109/ICMLC.2007.4370294

Zhu, H., Wang, Y., Wang, K. and Chen, Y., 2011. Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert Systems with Applications, 38(8), pp.10161-10169. DOI: https://doi.org/10.1016/j.eswa.2011.02.075

Fourie, P.C. and Groenwold, A.A., 2002. The particle swarm optimization algorithm in size and shape optimization. Structural and Multidisciplinary Optimization, 23, pp.259-267. DOI: https://doi.org/10.1007/s00158-002-0188-0

Ahmed, H. and Glasgow, J., 2012. Swarm intelligence: concepts, models, and applications. School Of Computing, Queens University Technical Report, pp. 1-38.

Arumugam, M.S. and Rao, M.V.C., 2006. On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems. Discrete Dynamics in Nature and Society, 2006(79295), pp. 1-17. DOI: https://doi.org/10.1155/DDNS/2006/79295

Agharkakli, A., Sabet, G.S. and Barouz, A., 2012. Simulation and analysis of passive and active suspension systems using quarter car model for different road profiles. International Journal of Engineering Trends and Technology, 3(5), pp.636-644.

Ahmed, A.A., 2021. Quarter car model optimization of active suspension system using fuzzy PID and linear quadratic regulator controllers. Global Journal of Engineering and Technology Advances, 6(3), pp.088-097. DOI: https://doi.org/10.30574/gjeta.2021.6.3.0041

Ahmed, M.I., Hazlina, M.Y. and Rashid, M.M., 2016. Mathematical modeling and control of active suspension system for a quarter car railway vehicle. Malaysian Journal of Mathematical Sciences, 10(5), pp.227-241.

Al-Mutar, W.H. and Abdalla, T.Y., 2015. Quarter car active suspension system control using fuzzy controller tuned by PSO. International journal of computer applications, 127(2), pp.38-43. DOI: https://doi.org/10.5120/ijca2015906334

Jiregna, I. and Sirata, G., 2020. A review of the vehicle suspension system. Journal of Mechanical and Energy Engineering, 4(2), pp. 109-114. DOI: https://doi.org/10.30464/jmee.2020.4.2.109

Liu, H., Gao, H. and Li, P., 2013. Handbook of vehicle suspension control systems. Institution of Engineering and Technology, pp. 1-397.

Gupta, A., Nitesh, K. D. & Deepak, A., 2022. Optimization in sliding mode control mechanism for reducing vibrations in the Quarter vehicle using Fuzzy Logic. International Journal of Food and Nutritional Sciences, Vol. 11, No. 1, pp. 3390-3401.

Yang, J., Li, J. and Du, Y., 2006. Adaptive fuzzy control of lateral semi-active suspension for high-speed railway vehicle. In Computational Intelligence: International Conference on Intelligent Computing, ICIC 2006 Kunming, China, August 16-19, 2006 Proceedings, Part II 2 (pp. 1104-1115). Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-540-37275-2_138

Eberhart, R. and Kennedy, J., 1995, October. A new optimizer using particle swarm theory. In MHS'95. Proceedings of the sixth International Symposium on micro machine and human science (pp. 39-43). Ieee.

Reynolds, C.W., 1987, August. Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp. 25-34. DOI: https://doi.org/10.1145/37401.37406

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Published

22-11-2023
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How to Cite

Agbroko, O. E., & Ogunti, E. O. (2023). Optimal Tuning of a LQR Controlled Active Quarter Car System Using Global Best Inertia Weight Modified Particle Swarm Optimization Algorithm. Journal of Engineering Advancements, 4(04), 116–123. https://doi.org/10.38032/jea.2023.04.002
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