Characterization and Optimization of Intelligent Dampers Based on Bionic Principles

From the perspective of human vibration perception, reducing vibration stimuli transmitted to occupants is essential for improving ride comfort and reducing fatigue. Intelligent dampers, as key actuators in semi-active suspension systems, provide adjustable damping capabilities for vibration control. This article combines them with biomimetic control principles to study the vibration control of semi-active suspension. The effects of damper forward and inverse models, damping force ranges, and time delays on suspension performance were analyzed. The results show that a function prediction-based damper model, a damping force range below 0.2 times and above 1.4 times the passive curve, and a 10 ms delay could balance vibration reduction and economy. Particle swarm optimization is used to optimize LQR control parameters for different road grades and typical speeds. Inspired by the adaptive behavior of chameleons, graded weights are assigned according to road characteristics, with greater emphasis on comfort on Grade A and B roads and driving stability on Grade C and D roads. The results show that proper matching of damper models and parameter constraints can fully exploit the adjustable damping capability of smart dampers. These findings provide a theoretical basis for designing and optimizing semi-active suspension control strategies.