This paper presents optimal adaptive fuzzy approaches combined by the predictive models for five degrees of freedom vehicle systems having a control force constraint of 1000 N on both front and rear suspensions in order to minimize the road disturbances. First, two separate adaptive fuzzy controllers are designed for the rear and front tires using the singleton fuzzifier, center average defuzzifier and product inference engine. The constructed fuzzy systems implement the adaptation laws based on the Lyapunov theory to guarantee the stability of the system. Afterward, a gravitational search optimization algorithm is applied to calculate the optimal values of the controller’s gains. The weighted summation of four objectives, as the relative displacement between the sprung mass and the front tire, the relative displacement between the sprung mass and the rear tire, the acceleration of the body and the acceleration of the seats, are regarded in the optimization process. Two different predictive models are employed to find the optimal design variables for the circumstances where the stability of the system is under variation. The first model is a fuzzy predictive system while the second one is based on the moving least squares interpolation. Eventually, the resultant online models are compared with the offline systems when the vehicle mass varies. These simulations obviously illustrate the efficiency and ability of the suggested strategy to remove the effect of the road disturbances on the ride comfort.
An optimal adaptive fuzzy controller is designed to achieve more stringent levels of comfort for a half-body car
model. This aim will be fulfilled by reducing road disturbances and decreasing the acceleration of the body. The
proposed controller consists of two adaptive fuzzy controllers with two fuzzy systems. Each one has two inputs,
one output and twenty five linguistic fuzzy IF-THEN rules. Every input has five Gaussian membership functions
and uses the product inference engine, singleton fuzzifier and the centre average defuzzifier. In order to determine
the optimal parameters for the Adaptive Fuzzy Controller (AFC), the Gravitational Search Algorithm (GSA) is
applied. The relative displacement between spring mass and tire, along with the acceleration of the body, are the
two objective functions being applied in the optimization algorithm. The results illustrate the superiority of the
proposed optimal adaptive fuzzy controller in comparison with traditional controllers.
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