Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory

  • Tim Chen
  • Chih Ching Hung
  • Yu Ching Huang
  • John C.Y. Chen
  • Samiur Rahman
  • Towfiqul Islam Mozumder

Abstract

In order to investigate and decide that the vehicle asymptotic vibration stability and improved comfort, the present paper deals with a fuzzy neural network (NN) evolved bat algorithm (EBA) backstepping adaptive controller based on grey signal predictors. The Lyapunov theory and backstepping method is utilized to appraise the math nonlinearity in the active vehicle suspension as well as acquire the final simulation control law in order to track the suitable signal. The Discrete Grey Model DGM (2,1) have been thus used to acquire prospect movement of the suspension system, so that the command controller can prove the convergence and the stability of the entire formula through the Lyapunov-like lemma. The controller overspreads the application range of mechanical elastic vehicle wheel (MEVW) as well as lays a favorable theoretic foundation in adapting to new wheels.

Author Biographies

Tim Chen
Faculty of Information TechnologyTon Duc Thang University, Ho Chi Minh City, Vietnam 
Chih Ching Hung
Department of Mechanical Engineering, National Taiwan University, Taipei, TaiwanFaculty of Electronic Engineering, Taipei Municipal Muzha Vocational High School, Taipei, Taiwan
Yu Ching Huang
Department of Earth Science, National Taiwan Normal University, Taipei, TaiwanCenter of Natural Science, Kaohsiung Municipal Fushan Junior High School, Kaohsiung, Taiwan
John C.Y. Chen
Department of Artificial IntelligenceUniversity of Maryland, USA
Samiur Rahman
School of Engineering and Physical SciencesNorth South University, Dhaka, Bangladesh
Towfiqul Islam Mozumder
School of Engineering and Physical SciencesNorth South University, Dhaka, Bangladesh

References

[1] Wang, Q.; Rajashekara, k.; Jia, Y.; Sun, J. (2017). Multiple quantile regression analyses of longitudinal data: heteroscedasticity and efficient estimation, IEEE Transactions on Vehicle Technologies, 66(9), 2017, 7722-7729.
https://doi.org/10.1109/TVT.2017.2688416

[2] Pfluger, J.; Savelsberg, R.; Hülshorst, T.; Pischinger, S., & Andert, J. (2018). Potential of Real- Time Cylinder Pressure Analysis by Using Field Programmable Gate Arrays, International Journal of Automotive Technology, 19, 643-650.
https://doi.org/10.1007/s12239-018-0061-9

[3] Silva, G. J.; Datta, A.; Bhattacharyya, S. P. (2002). New results on the synthesis of PID controllers, IEEE Transactions on Automation Control, 47(2), 241-252, Aug. 2002.
https://doi.org/10.1109/9.983352

[4] Chen, T.; Kuo, D.; Chen, C. Y. J. (2021a). Fuzzy C-means robust algorithm for nonlinear systems, Soft Computing, 25(11), 7297-7305.
https://doi.org/10.1007/s00500-021-05655-y

[5] Chen, T.; Huang, Y. C.; Chen, C. Y. J. (2021b). Smart structural stability and NN based intelligent control for nonlinear systems, Smart Structures and Systems, 27.

[6] Chen, T.; Huang, Y. C.; Chen, C. Y. J. (2021c). Wind vibration control of stay cables using an evolutionary algorithm, Wind and Structures, 32(1), 73-86.

[7] Jiang, Y.; Yang, C.; Ma, H. (2016). A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems, Discrete Dynamics in Nature and Society, 2016, 1-11.
https://doi.org/10.1155/2016/7217364

[8] Wang, T.; Sui, S.; Tong, S. (2017). Data-based adaptive neural network optimal output feedback control for nonlinear systems with actuator saturation, Neurocomputing, 247, 192-201.
https://doi.org/10.1016/j.neucom.2017.03.053

[9] Kaur, R.; Jyoti, Navjot. (2016). Image Segmentation using Hybrid Particle Swarm Optimization & Penalized Fuzzy C-Mean Clustering, International Journal of Engineering Research and General Science, 4(1), 256-261, 2091-2730, 2016.

[10] Feng, H. H.; Feng, H.; Horng, J.; Jou, S. (2012). Bacterial Foraging Particle Swarm Optimization Algorithm Based Fuzzy-VQ Compression Systems, Journal of Information Hiding and Multimedia Signal Processing, 3, 227-239.

[11] Li, R. J.; Lee, E. S. (1989). Analysis of Fuzzy Queues, Computers and Mathematics with Applications, 17(7), 1143-1147.
https://doi.org/10.1016/0898-1221(89)90044-8

[12] Chen, T.; Chen, C. Y. J. (2021). Optimized AI controller for reinforced concrete frame structures under earthquake excitation, Advances in Concrete Construction, 11(1), 1-9, 2021
https://doi.org/10.1155/2021/6620030

[13] Kazemian, A. H.; Fooladi, M.; Darijani, H. (2019). Non-linear control of vehicle's rollover using sliding mode controller for new 8 degrees of freedom suspension model, International Journal of Heavy Vehicle Systems, 26, 707.
https://doi.org/10.1504/IJHVS.2019.10023141

[14] Latrech, C.; Kchaou, M.; Guéguen, H. (2018). Networked non-fragile H1 static output feedback control design for vehicle dynamics stability: A descriptor approach, European Journal of Control, 40, 13-26.
https://doi.org/10.1016/j.ejcon.2017.10.005

[15] Li, W.; Xie, Z.; Wong, P.; Ma, X.; Cao, Y.; Zhao, J. (2019). Nonfragile H1 Control of Delayed Active Suspension Systems in Finite Frequency Under Nonstationary Running, Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 141, 061001.
https://doi.org/10.1115/1.4042468

[16] Chen, T.; Rao, S.; Sabitovich, R. T.; Chapron, B.; Chen, C. Y. J. (2020). An Intelligent Algorithm Optimum for Building Design of Fuzzy Structures, Iranian Journal of Science and Technology, Transactions of Civil Engineering , 44, 523-531, 2020.
https://doi.org/10.1007/s40996-019-00251-5

[17] Chen, T.; Khurram, S.; Pandey, L.; Chen, J. C. Y. (2020). LMI based criterion for reinforced concrete frame structures, Advances in Concrete Construction, 9(4), 407-412.

[18] Chen, T.; Babanin, A.; Muhammad, A.; Chapron, B., Chen, C. Y. J. (2020). Evolved fuzzy NN control for discrete-time nonlinear systems based on fuzzy Lyapunov methods, Journal of Circuits, Systems and Computers, 29(1), 2050015.
https://doi.org/10.1142/S0218126620500152

[19] Chen, C. W. (2014). A criterion of robustness intelligent nonlinear control for multiple time-delay systems based on fuzzy Lyapunov methods, Nonlinear Dynamics, 76, 23-31.
https://doi.org/10.1007/s11071-013-0869-9

[20] Xia, X.; Liu, P.; Zhang, N.; Ning, D.; Zheng, M.; Du, H. (2019). Takagi-Sugeno Fuzzy Control for the Semi-active Seat Suspension with an Electromagnetic Damper, 3rd Conference on Vehicle Control and Intelligence (CVCI), 1-6.
https://doi.org/10.1109/CVCI47823.2019.8951590

[21] Chatterjee, A.; Chatterjee, R. (2008). Augmented Stable Fuzzy Control for Flexible Robotic Arm Using LMI Approach and Neuro-Fuzzy State Space Modeling, IEEE Transactions On Industrial Electronics, 55(3), 3671-3681, March 2008.
https://doi.org/10.1109/TIE.2007.896439

[22] Haidegger, T.; Kovács, L.; Precup, R. E.; Preitl. S.; Benyó, B.; Benyó, Z. (2011). Cascade control for telerobotic systems serving space medicine, IFAC Proceedings Volumes, 44(1), 3759-3764, January 2011.
https://doi.org/10.3182/20110828-6-IT-1002.02482

[23] Precup, R. E.; Tomescu, M. L. (2015). Stable fuzzy logic control of a general class of chaotic systems, Neural Computing and Applications, 26(3), 541-550, 2015.
https://doi.org/10.1007/s00521-014-1644-7

[24] Sun, W.; Gao, H.; Kaynak, O. (2013). Adaptive backstepping control for active suspension systems with hard constraints, IEEE/ASME Transactions on Mechatronics, 18(3), 1072-1079, 2013.
https://doi.org/10.1109/TMECH.2012.2204765

[25] Zhao, Y.; Deng, Y.; Lin, F.; Zhu, M.; Xiao, Z. (2018). Transient Dynamic Characteristics of a Non- Pneumatic Mechanical Elastic Wheel Rolling Over a Ditch, International Journal of Automotive Technology, 19, 499-508.
https://doi.org/10.1007/s12239-018-0048-6

[26] Dzitac, I.; Filip, F. G.; Manolescu, M. J. (2017). Fuzzy logic is not fuzzy: World-renowned computer scientist Lotfi A. Zadeh, International Journal of Computers Communications & Control, 12(6), 748-789, 2017.
https://doi.org/10.15837/ijccc.2017.6.3111

[27] Khosravi, A.; Nahavandi, S.; Creighton, D.; Srinivasan, D. (2012). Interval type-2 fuzzy logic systems for load forecasting: A comparative study, IEEE Transactions on Power Systems, 27(3), 1274-1282, 2012.
https://doi.org/10.1109/TPWRS.2011.2181981

[28] Liang, Q.; Mendel, J. M. (1999). An introduction to type-2 TSK fuzzy logic systems, IEEE International Fuzzy Systems, Conference Proceedings (Cat. No. 99CH36315), IEEE, 1999.

[29] Haidegger, T.; Kovács, L.; Preitl, S.; Precup, R. E.; Benyo, B.; Benyo, Z.; Controller design solutions for long distance telesurgical applications, International Journal of Artificial Intelligence, 6(11), 48-71, March 2011.

[30] Yu, J.; Zhang, K.; Fei, S. (2008). Adaptive Fuzzy Tracking Control of a Class of Stochastic Nonlinear Systems with Unknown Dead-Zone Input, International Journal of Fuzzy Systems, 10, 18-23.

[31] Li, H.; Jing, X.; Lam, H.; Shi, P. (2014). Fuzzy sampled-data control for uncertain, IEEE Transactions on Cybernetics, 44(7), 1111-1126, 2014.
https://doi.org/10.1109/TCYB.2013.2279534

[32] Li, H.; Jing, X.; Karimi, H. R. (2014). Output-Feedback-Based H1Control for Vehicle Suspension Systems With Control Delay, IEEE Transactions on Industrial Electronics, 61(1), 436-446, 2014.
https://doi.org/10.1109/TIE.2013.2242418

[33] Yu, E.; Ambati, A.; Andersen, M. S.; Krohn, L.; Estiar, M. A.; Saini, P.; Senkevich, K.; Sosero, Y. L.; Sreelatha, A. A.; Ruskey, J.; Asayesh, F.; Spiegelman, D.; Toft, M.; Viken, M. K.; Con, T. I.; Sharma, M.; Blauwendraat, C.; Pihlstrøm, L.; Mignot, E.; Gan-Or, Z. (2020). Fine mapping of the HLA locus in Parkinson's disease in Europeans, medRxiv, November 03, 2020.
https://doi.org/10.1101/2020.10.29.20217059

[34] Wu, F.; Chang, G.; Deng, K.; Tao, W. (2019). Selecting data for autoregressive modeling in polar motion prediction, Acta Geodaetica et Geophysica, 54(4), 557-566.
https://doi.org/10.1007/s40328-019-00271-7

[35] Shao, Y.; Wei, Y.; Su, H. (2010). On Approximating Grey Model DGM(2,1), Journal of Grey Systems: Elsevier, 22(4), 333-340, 2010.

[36] Li, K.; Zhang, T. (2018). Forecasting Electricity Consumption Using an Improved Grey Prediction Model, Information: MDPI, 9(8), 204, 2018.
https://doi.org/10.3390/info9080204

[37] Chen, C. W. (2014). Interconnected TS fuzzy technique for nonlinear time-delay structural systems, Nonlinear Dynamics, 76, 13-22, 2014.
https://doi.org/10.1007/s11071-013-0841-8

[38] Tsai, P. W.; Pan, J. S.; Liao, B. Y.; Tsai, M. J.; Vaci, I. (2012). Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems, Applied Mechanics and Materials, 148-149, 134- 137, 2012.
https://doi.org/10.4028/www.scientific.net/AMM.148-149.134

[39] Tsai, P. W.; Chen, C. W. (2014). A Novel Criterion for Nonlinear Time-Delay Systems Using LMI Fuzzy Lyapunov Method, Applied Soft Computing, 25, 461-472.
https://doi.org/10.1016/j.asoc.2014.08.045

[40] Tsai, P. W.; Alsaedi, A.; Hayat, T.; Chen, C. W. (2016). A Novel Control Algorithm for Interaction between Surface Waves and a Permeable Floating Structure, China Ocean Engineering, 30(2), 161-176.
https://doi.org/10.1007/s13344-016-0009-7

[41] Chen, T.; Chen, J. C. Y. (2020). On the algorithmic stability of optimal control with derivative operators, Circuits Systems and Signal Process, 39, 5863-5881, 2020.
https://doi.org/10.1007/s00034-020-01447-1

[42] Chen, T.; Crosbie, R. C.; Anandkumarb, A.; Melville, C.; Chan, J. (2021). Optimized AI controller for reinforced concrete frame structures under earthquake excitation, Advances in Concrete Construction, 11(1), 1-9, January 2021.
Published
2021-06-08
How to Cite
CHEN, Tim et al. Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 3, june 2021. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3991>. Date accessed: 22 may 2022.