Effect of Preview Digital System Controller Design on the Performance of MR Suspension System

This study examines the design of the digital system controller for the MR semi-active suspension system. The inverse Recurrent Neural Network (RNN) damper controller is integrated with RNN digital signals system controller design. The system controller determines the desired damping force, which the damper controller then tracks through the generation and adjustment of the command voltage. Before their signals are converted to digital, a low-pass filter is employed, and the digital signals of the sigmoid function are then trained by an RNN system controller. Because the digital signal processing of the microcomputer is often more dependable, economical, and smaller than that of an analogue controller, a digital system controller is designed here. The Aliasing error does not occur when using an anti-aliasing filter before the analog/digital converter (A/D), which is an analog low-pass filter. The study's 0.01 sampling time is consistent with a broad range of frequencies, particularly at 1 Hz and 10 Hz, which correspond to the human body's sensitive frequency and the wheel's resonance frequency. The simulation results demonstrate that of all the systems looked at, the preview digital system controller designed coupled to the RNN damper controller enhances both ride comfort and vehicle stability.