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In this paper, the performance of a controlled air suspension system is integrated with the controlled braking system. In order to improve both ride comfort and dynamic stability, the neural network (NN)-predictive control is designed as a system controller for the air suspension system to minimize vertical, pitch, and roll motions. The rate of controlled force generated by the air suspension system is changed according to external excitation transmitted from road roughness to the vehicle body. PID controller is designed for the antilock braking system (ABS) to improve braking performance. Interval type-2 fuzzy control system (IT-2FCS) is also designed as an integrated controller to generate desired paths for both the NN-predictive controller and PID controller. Desired paths are achieved based on tuned dynamic responses of the vehicle suspension system and the relative skid ratio.

In order to achieve the high capability of the ride comfort and regulating the tire slip ratio, a preview of a nonlinear semi-active vibration control suspension system using a magnetorheological (MR) fluid damper is integrated with traction control in this paper. A controlled semi-active suspension system, which consists of the system controller and damper controller, was used to develop ride comfort, while the traction controller is utilized to reduce a generated slip between the vehicle speed and rotational rate of the tire. Both Fractional-Order Filtered Proportional-Integral-Derivative (P¯IλDμ) and Fuzzy Logic connected either series or parallel with P¯IλDμ are designed as various methodologies of a system controller to generate optimal tracking of the desired damping force. The signum function method is modified as a damper controller to calculate an applied input voltage to the MR damper coil based on both preview signals and the desired damping force tracking.