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Comparison of Control Strategies Applied to Nonlinear Quarterly Car Passive Suspension System

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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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Suspension System; Fuzzy System; Neural Network; NPID

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