International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Real-Time Adaptive Suspension Control Framework: Harnessing a Neural Network Digital Twin for Aero-Dynamic Ride Optimization

Author(s) Mr. Akshey Sharma Kasibhatla
Country India
Abstract The pursuit of optimal vehicle dynamics necessitates the real-time balancing of ride comfort, handling stability, and aerodynamic efficiency. Traditional suspension control systems often rely on pre-tuned, static models that fail to adapt to dynamic road conditions and changing aerodynamic loads (e.g., due to vehicle speed, crosswinds, or load distribution). This paper proposes a novel framework utilizing a Digital Twin (DT) integrated with an Adaptive Neural Network (ANN) controller for real-time aero-dynamic ride optimization. The DT, a high-fidelity virtual replica of the physical vehicle, incorporates real-time sensor data (accelerometers, LiDAR/Vision for road profile, vehicle speed, and active aerodynamic surfaces). The ANN is trained on this DT data to predict optimal semi-active/active suspension damping and spring rate adjustments that instantaneously counteract changes in aerodynamic downforce and drag. This approach is expected to significantly enhance ride quality and stability over varying speeds and conditions while simultaneously minimizing aerodynamic performance penalties, outperforming traditional Skyhook and linear control strategies.
Keywords • Digital Twin (DT) • Neural Network (NN) Control • Adaptive Suspension System • Aerodynamic Optimization • Vehicle Dynamics • Real-Time Control • Active Suspension • Ride Comfort
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-20
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.63869
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E-ISSN 2582-2160
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