Reliability-Aware Adaptive Model Predictive Control of Full-Vehicle Active Suspension via TD3 Weight Regulation and Posture Feedback

Abstract

Full-vehicle active suspension control requires coordinated regulation of ride comfort, body posture, suspension travel, tire road holding, and control effort. To improve the adaptability of fixed-weight MPC while retaining model-based interpretability, this paper proposes a TD3-MPC posture-feedback controller for a seven-degree-of-freedom full-vehicle active suspension system. The TD3 Actor is trained offline and deployed with fixed network parameters to generate five bounded actions for online MPC weight regulation rather than directly outputting actuator forces. The MPC layer calculates the constrained four-wheel baseline forces, after which posture feedback compensates for vehicle-body vertical, pitch, and roll responses. A composite reward balances body acceleration, suspension deflection, tire deformation, energy control, force variation, and action smoothness. The controller is evaluated through time-domain, frequency-domain, parameter-perturbation, ablation, and Monte Carlo analyses. Under the nominal Class-D Road condition at 20 m/s, it reduced the RMS values of body vertical, pitch, and roll angular accelerations by 13.24%, 4.77%, and 10.68%, respectively, and reduced control energy by 3.06% relative to fixed-weight MPC. Across 360 paired cases covering ISO Class-B-D roads and speeds of 10–25 m/s, the corresponding mean reductions were 9.55%, 6.30%, and 8.37%, respectively.