Biogeography-based optimized adaptive neuro-fuzzy control of a nonlinear active suspension system
Keywords:
Active Suspension System, Optimal Vibration Control, Biogeography-Based Optimization, Fuzzy c-means clustering, ANFIS
Abstract
This paper presents an optimum network structure based on a BBO tuned adaptive neuro-fuzzy inference system (ANFIS) to control an active suspension system (ASS). The unsupervised learning via Biogeography-Based Optimization (BBO) algorithm is used to train the ANFIS network. The optimal proportional-integral-derivative controller tuned based on the LQR method is used to generate the training data set. ANFIS base on Fuzzy c-means (FCM) clustering algorithm is applied to approximate the relationships between the vehicle body (sprung mass) vertical input velocity and the actuator output force. BBO algorithm is used to optimize fuzzy c means clustering parameters. The numerical simulation results showed that the proposed optimized BBO-FCMANFIS based vehicle suspension system has better performance as compared with the optimal LQR-PID controller under uncertainties in both of reducing actuator energy consumption and the suppression of the vibration of the sprung mass acceleration, with a 43% and 9.5% reduction, respectively.
References
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[2] M. Geravand and N. Aghakhani, “Fuzzy sliding mode control for applying to active vehicle suspentions, ” WSEAS Trans Syst Control, vol. 5, pp. 48-57, 2007.
[3] W. Sun, J. Li, Y. Zhao, and H. Gao, “Vibration control for active seat suspension systems via dynamic output feedback with limited frequency characteristic, ” Mechatronics, vol. 21, pp. 250-260, 2010.
[4] M. Senthil kumar, “Genetic algorithm-based proportional derivative controller for the development of active suspension system," Inf Technol Control, vol. 36, pp. 58-67, 2007.
[5] G. Priyandoko, M. Mailah, and H. Jamaluddin,“Vehicle active suspension system using skyhook adaptive neuro-active force control, ” Mech Syst Signal Process, vol. 23, pp. 855-868, 2009.
[6] S. Kumar, K. P. S. Rana, J. Kumar et al., “Self-tuned robust fractional order fuzzy PD controller for uncertain and nonlinear active suspension system, ” Neural Comput & Applic, vol. 30, pp. 1827-1843, 2018.
[7] A. A. Aldair, E. B. Alsaedee, and T.Y. Abdalla, “"Design of ABCF Control Scheme for Full Vehicle Nonlinear Active Suspension System with Passenger Seat, ” Iran J Sci Technol Trans Electr Eng, vol. 43, pp. 289-302, 2019.
[8] J. Mrazguaa, E. Houssaine-Tisssira, and M. Ouahi, “Fuzzy Fault-Tolerant H∞ Control Approach for Nonlinear Active Suspension Systems with Actuator Failure, ” Procedia Comput Sci, vol. 148, pp. 465-474, 2019.
[9] J. Na, Y. Huang, X. Wu et al., “Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay, ” IEEE Trans Cybern, vol. 50, pp. 2639–2650, 2019.
[10] Y. Qin, J.J. Rath, C. Hu et al., “Adaptive nonlinear active suspension control based on a robust road classifier with a modified super-twisting algorithm, ” Nonlinear Dyn, vol. 97, pp. 425-2442, 2019.
[11] D. Singh, “Modeling and control of passenger body vibrations in active quarter car system: a hybrid ANFIS PID approach, ” Int J Dynam Control, vol. 6, pp. 1649-1662, 2018.
[12] D. Singh, “Whole body active vibration control of passenger biodynamics in quarter car model under random road excitations using ANFIS gain tuned PID-super twisting control, ” Int J Dynam. Control, vol. 8, pp.999–1012, 2020.
[13] Q. Wang, Y. Zhao, H. Xu et al., “Adaptive backstepping control with grey signal predictor for nonlinear active suspension system matching mechanical elastic wheel, ” Mech Syst Signal Process, vol. 131, pp. 97-111, 2019.
[14] S. Liu, T. Zheng, D. Zhao et al., “Strongly perturbed sliding mode adaptive control of vehicle active suspension system considering actuator nonlinearity, ” Veh Syst Dyn (2020), 10.1080/00423114.2020.1840598
[15] Sy. Dzung Nguyen, B. Danh-Lam, S.B. Choi, “Smart dampers-based vibration control -Part 2: Fractional order sliding control for vehicle suspension system,” Mech Syst Signal Process, vol. 148, (2021), https://doi.org/10.1016/j.ymssp.2020.107145
[16] P. Swethamarai, P. Lakshmi,“ "Adaptive-fuzzy fractional order PID controller-based active suspension for vibration control,” IETE J Res (2020), https://doi.org/10.1080/03772063.2020.1768906
[17] M. J. Mahmoodabadi and N. Nejadkourki, "Optimal fuzzy adaptive robust PID control for an active suspension system," Aust J Mech Eng (2020), https://doi.org/10.1080/14484846.2020.1734154
[18] H. Pang, X. Zhang, Z. Xu, “Adaptive backstepping-based tracking control design for nonlinear active suspension system with parameter uncertainties and safety constraints,” ISA Trans, vol. 88, pp. 23-36, 2019.
[19] Y. Kuo and T. Li, “GA based Fuzzy PI/PD controller for automotive active suspension system,” IEEE Trans Ind Electron, vol. 46, pp.1051-1056, 1999.
[20] J. Feng and F. Yu, “GA-based PID and fuzzy logic controller for active vehicle suspension system,” Int J Automot Technol, vol. 4, pp. 181-191, 2003.
[21] J. S. Lin and I. Kanellakopoulos, “Nonlinear design of active suspension,” IEEE Control Syst, vol. 17, pp. 45-59, 1997.
[22] C. Kim and P. I. Ro, “A sliding mode controller for vehicle active suspension systems with nonlinearities,” Proc IMechE Part D: J Autom. Eng, vol. 212, pp. 79-92, 1998.
[23] T. Yoshimura, A. Kume, M. Kurimoto, et al.,“Construction of an active suspension system of a quarter car model using the concept of sliding mode control,” J Sound Vib, vol. 239, pp. 187-199, 2001.
[24] S. J. Huang and W. C. Lin, “Adaptive fuzzy controller with sliding surface for vehicle suspension control,” IEEE Trans Fuzzy Syst, vol. 11, pp. 550-559, 2003.
[25] J. Lin, R. J. Lian, C. N. Huang, , et al., “Enhanced fuzzy sliding mode controller for active suspension systems,” Mechatronics, vol. 19, pp. 1178-1190, 2009.
[26] S. J. Huang and W. C. Lin, “A neural network based sliding mode controller for active vehicle suspension,” Proc IMechE Part D: J Autom Eng, vol. 221, pp. 1381-1397, 2007.
[27] F. J . D’Amato and D. E. Viassolo, “Fuzzy control for active suspensions, ” Mechatronics, vol. 10, pp. 897-920, 2000.
[28] I. Eski and S. Yıldırım, “Vibration control of vehicle active suspension system using a new robust neural network control system,” Simulat Model Pract Theor, vol. 17, pp. 778-793, 2009.
[29] A. Konoiko, A. Kadhem, I. Saiful, et al., "Deep learning framework for controlling an active suspension system,” J Vib Control, vol. 25, pp. 2316-2329, 2019.
[30] Y. Zhang and A. Kandel "Compensatory neurofuzzy systems with fast learning algorithms," IEEE Trans Neural Netw Syst vol. 9, pp. 80-105, 1998.
[31] A .A. Aldair, W. J. Wang, “Design an intelligent controller for full vehicle nonlinear active suspension systems, ” Int J Smart Sens Intell Syst, vol. 4, pp. 224-243, 2011.
[32] R. Kothandaraman and L. Ponnusamy, "PSO tuned adaptive neuro-fuzzy controller for vehicle suspension systems, ” J Adv Info, vol. 3, pp. 57-63, 2011.
[33] R. Kalaivani and P. Lakshmi, “Adaptive neuro-fuzzy controller for vehicle suspension system, ” in Proc. 2013 IEEE International Conference on Advanced Computing (ICAC), Chennai, India, 18-20 Dec. 2013.
[34] U. Rashid, M. Jamil, and S. Gilani, “LQR based training of adaptive neuro-fuzzy controller, ” J Intell Fuzzy Syst, vol. 54, pp. 311-322, 2016.
[35] M. Cui, L. Geng, and Z. Wu, “Random Modeling and Control of Nonlinear Active Suspension," Math Probl Eng, (2017), https://doi.org/10.1155/2017/4045796
[36] S. Yan, E. L. Dowell, and B. Lin, “Effects of nonlinear damping suspension on nonperiodic motions of a flexible rotor in journal bearings,” Nonlinear Dyn, vol. 78, pp. 1435–1450, 2014.
[37] G.D. Nusantoro and G. Priyandoko, “PID state feedback controller of a quarter car active suspension system,” J Basic Appl Sci Res, vol. 1, pp. 2304-2307, 1994.
[38] D. Simon, “Biogeography based optimization, ” IEEE Trans Evol Comput, vol. 12, pp. 702-713, 2008.
Published
2021-09-01
How to Cite
Fayazi, A., & Ghayoumi Zadeh, H. (2021). Biogeography-based optimized adaptive neuro-fuzzy control of a nonlinear active suspension system. Majlesi Journal of Mechatronic Systems, 10(3), 41-52. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/506
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Articles
