DESIGN OF A SEMI-ACTIVE SUSPENSION CONTROL METHOD BASED ON AN ENHANCED INVERSE MODEL FOR NONLINEAR MAGNETORHEOLOGICAL DAMPERS

The nonlinear hysteresis characteristics of magnetorheological dampers lead to low fitting accuracy and poor practicality of their inverse models. Hence, to improve the accuracy of an inverse model generated with BP neural network, this research presents a novel optimization approach called Beluga Whale Optimization. The prediction accuracy of current is enhanced by the optimized inverse model. Under the enhanced inverse model, a variable universe fuzzy PID control is created. Based on the research outcomes, it has been shown that the introduction of control contributes to noteworthy improvements in the suspension performance metrics, both in terms of time and frequency domains.