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In this paper, a nonlinear semi-active vehicle suspension system using MR fluid dampers is investigated to enhance ride comfort and vehicle stability. Fuzzy logic and fuzzy self-tuning PID control techniques are applied as system controllers to compute desired front and rear damping forces in conjunction with a Signum function method damper controller to assess force track-ability of system controllers. The suggested fuzzy self-tuning PID operates fuzzy system as a PID gains tuner to mitigate the vehicle vibration levels and achieve excellent performance related to ride comfort and vehicle stability. The equations of motion of four-degrees-of-freedom semi-active half-vehicle suspension system incorporating MR dampers are derived and simulated using Matlab/Simulink software.

A full vehicle of a preview control semi-active suspension system based on an interval type-2 fuzzy controller design using a magnetorheological (MR) damper to improve ride comfort is investigated in this paper. It is integrated with the force distribution system to obtain the optimal rate of road adhesion during braking and handling. The nonlinear suspension model is derived by considering vertical, pitch, and roll motions. The preview interval type-2 fuzzy technique is designed as a system controller, and it is attached with a Signum function method as a damper controller to turn on the voltage for the MR damper. This voltage is adjusted for each wheel based on the external excitation generated by road roughness in order to enhance ride comfort. To describe the effectiveness and adaptable responses of the preview controlled semi-active system, the performance is compared with both the passive and MR passive suspension systems during time and frequency domains.

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.

A non-linear mathematical model of a semi-active (2DOF) vehicle suspension using a magnetorheological (MR) damper with information concerning the road profile ahead of the vehicle is proposed in this paper. The semi-active vibration control system using an MR damper consists of two nested controllers: a system controller and a damper controller. The fuzzy logic technique is used to design the system controller based on both the dynamic responses of the suspension and the Padé approximation algorithm method of a preview control to evaluate the desired damping force. In addition, look-ahead preview of the excitations resulting from road irregularities is used to quickly mitigate the effect of the control system time delay on the damper response.

In this paper, semi-active MR main suspension system based on system controller design to minimize pitch motion linked with MR-controlled seat suspension by considering driver’s biodynamics is investigated. According to a fixed footprint tire model, the transmitted tire force is determined. The linear-quadratic Gaussian (LQG) system controller is able to enhance ride comfort by adjusting damping forces based on an evaluation of body vibration from the dynamic responses. The controlled damping forces are tracked by the signum function controllers to evaluate the supply voltages for the front and rear MR dampers. Based on the sprung mass acceleration level and its derivative as the inputs, the optimal type-2 (T-2) fuzzy seat system controller is designed to regulate the controlled seat MR damper force.