Physics-based hierarchical identification and model-inversion feedforward control for CDC semi-active dampers

Abstract

Semi-active suspension systems equipped with continuous damping control (CDC) dampers place stringent demands on accurate damper-level force tracking. However, the inherent hysteresis, compression-rebound asymmetry, and strong current dependence of the damper force make force control persistently challenging. This paper addresses this challenge with a four-component dynamic model that describes elastic restoring force, velocity-dependent viscous damping force, Bouc-Wen hysteresis force, and an additional force induced by directional asymmetry in a unified framework. Each current-dependent parameter is represented by an exponential saturation function, which naturally ensures bounded and monotonic coefficient mappings at the model level while avoiding empirical clipping. Because the four force components dominate in distinct operating regions, a three-stage hierarchical parameter identification method is further proposed to replace the coupled, single-step identification of all 20 parameters by direct global optimization. Based on the identified model, a feedforward inverse controller is constructed that combines online Bouc-Wen state reconstruction, passivity-based clipping, and Newton iteration to achieve real-time current solution and force tracking. Replacing equal-weight global fitting with the proposed saturation-mapped hierarchical scheme substantially reduces the identification error, from 0.155 to 0.079. In simulation, the inverse controller built on this model attains a force-tracking RMSE of 4.9 N, and hardware-in-the-loop experiments further verify the feasibility and effectiveness of the proposed framework under power-amplifier transport delay and other bench-level non-idealities.

Keywords

CDC damper / Semi-active suspension / Hierarchical parameter identification / Bouc-Wen model / Model-inversion feedforward control / Force-sensor-free force tracking

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