摘要
Conventional vehicle suspension systems,often relying on integer-order mod-els with fixed damping coefficients,struggle to deliver optimal performance across diverse and dynamic road conditions.This paper introduces a novel intelligent adaptive suspension framework that leverages fractional-order cal-culus and real-time optimization.The core of the system is a damping model employing a Caputo fractional derivative of orderα∈(1,2),whereαitself is dynamically tuned.This adaptation is driven by an Extremum Seeking Con-trol(ESC)algorithm,which continuously adjustsαto minimize a prede-fined cost function reflecting ride comfort and road holding,based on fused sensor data(e.g.,from IMUs and wheel encoders processed via a Kalman Fil-ter).This model-free online optimization allows the suspension to adapt its fundamental damping characteristics to changing terrains without requiring explicit road classification models.Simulation results for a quarter-car model demonstrate the ESC’s ability to converge towards an optimalα,enhancing the suspension’s adaptability and performance across varying operating sce-narios,thereby indicating a promising path for next-generation terrain-aware vehicle dynamics control.This model-free,online optimization allows the sus-pension to adapt its fundamental damping characteristics to changing terrains without requiring explicit road classification models.Real-time feasibility is achieved through computationally efficient numerical approximations of the fractional derivative and the inherent filtering within the ESC loop,making the framework suitable for implementation on modern automotive control-lers.Simulation results for a quarter-car model demonstrate the ESC’s ability to converge towards an optimalα,enhancing the suspension’s adaptability and performance across varying operating scenarios,thereby indicating a promising path for next-generation terrain-aware vehicle dynamics control.
