Enhanced Seat Suspension Performance Through Positive Real Network Optimization and Skyhook Inertial Control

To solve the low frequency vibration problem faced by heavy truck drivers, a positive real network inertial suspension structure combined with a skyhook inertial control strategy is adopted. This integrated approach effectively reduces low-frequency vibrations at the seat and human body levels. Specifically, this research aims to mitigate the acceleration experienced on the seat surface within the low-frequency range. Firstly, a human–seat dynamics model is established. Subsequently, based on the principles of network synthesis, the derivation of transfer functions for both first- and second-order systems is discussed, and the network parameters are also optimized. This paper further compares the optimization outcomes of first- and second-order skyhook seat inertial suspensions. An adaptive fuzzy sliding-mode controller (AFSMC) has been developed for an electromechanical inerter, ensuring it closely tracks optimal control performance. The findings demonstrate that the new suspension system achieves a 29.9% reduction in the root-mean-square value of seat surface acceleration and a 43.1% decrease in the road-bump peak acceleration compared to a conventional suspension system. The results show that the inertial suspension with skyhook inertial control is highly effective in completely suppressing seat surface acceleration within the low-frequency domain.