Comparison of Control Strategies Applied to Nonlinear Quarterly Car Passive Suspension System
Road unevenness causes wheel and engine vibration which in turn lead to internal noise in automobiles. Developing a reliable control algorithm for vehicle suspension system is quite cumbersome due to the nonlinear nature of the system coupled with the issue of improving compromise between the conflicting demands of ride comfort and road handling. This paper presents a comparison of Fuzzy, Neural Network (NN) inverse control and Nonlinear Proportional Integral Derivative (NPID) control applied to the quarterly car passive suspension system. The performances of the controllers were evaluated based on rise time, settling time and peak time as illustrated via set-points tracking. The simulation results indicated that the performances of the Fuzzy controller and NPID are slightly better than neural network inverse control which could be due to lack of obtaining an accurate inverse plant model. The Fuzzy controller and NPID may serve as an alternative and valuable control schemes for the system
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A. Florin and P. Liliana: Passive Suspension Modelling using MATLAB Quarter Car Model Input Signal Step Type, Teh. Technol. Prod. Mach. Manuf. Technol., Vol. 45 (2013) p. 258–263.
F. L. Bernard. “Control system design.” Published: New York. 1987.
M. Pable and P. Seshu: Design of Passive Suspensions to Reduce Actuator Control Effort, 12th IFToMM World Congress, 2007.
S. Trimp, et-al. "A self-Tunning LQR approach demostrated on an inverted pendulum" 19th International Federation of Automatic Control (IFAC), Cape Town South Africa. pp.11281-11287. August, 2014.
Kajan, S., Dideková, Z., Kozák, S., Linder, M., Neural-genetic control algorithm of nonlinear systems, (2013) International Review of Automatic Control (IREACO), 6 (2), pp. 206-210.
L. Zadeh: Fuzzy sets, Inf. Control, Vol. 353, pp. 338–353, 1965.
P.Van Overschee et al., RAPID: The end of heuristic PID tuning, Journal A, vol.38, no3, pp6-10.
J. Mendel, “Fuzzy logic systems for engineering: a tutorial,” Proc. IEEE, no. 9408047, 1995.
L. Zadeh: The concept of a linguistic variable and its application to approximate reasoning—I, Inf. Sci. (Ny)., Vol. 8, no. 3, pp. 199–249, Jan. 1975.
A. Abraham: Nature and scope of AI techniques, in Handbook for measurement systems designing system design, P. S. and R. Thorn, Ed. London: John Wiley & Sons Ltd, 2005, p.893–900.
A. Engelbrecht: Computational intelligence: An Introduction. 2007.
E. Mamdani and S. Assilian: An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man. Mach. Stud. (1975) p. 1–13.
M. Sugeno: Industrial applications of fuzzy control, Elsevier Science Inc New York, NY,USA., 1985.
Cigánek, J., Noge, F., Kozák, Å ., Modeling and control of mechatronic systems using fuzzy logic, (2014) International Review of Automatic Control (IREACO), 7 (1), pp. 45-51.
G. Chen and T. Pham: Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, New York 2001.
Yusuf, L.A., Magaji, N., Comparison of fuzzy logic and GA-PID controller for position control of inverted pendulum, (2014) International Review of Automatic Control (IREACO), 7 (4), pp. 380-385.
Heloisa H. Müllera, Marcos J. Rider, Carlos A. Castroa, Artificial neural networks for load flow and external equivalents studies, Electric Power Systems Research. Vol. 80, No. 9, Sep 2010, pp.1033-1041.
Y. X. Su, D. Sun, and B. Y. Duan: Design of an enhanced nonlinear PID controller, Mechatronics, vol. 15, pp. 1005–1024, 2005.
A. Lopez P. Murrill and C. Smith, Tuning PI and PID digital controllers Instruments And control vol.42 pp89-95 1969.
Wu, Y., Huang, C., Deng, W., Cui, G., MATLAB RTW tool application to configure RBF Neural Network PID controller of turntable servo system, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2750-2754.
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