Digital Twin-based Control Co-Design of Full Vehicle Active Suspensions via Deep Reinforcement Learning

arXiv — cs.LG•Thursday, December 4, 2025 at 5:00:00 AM

PositiveArtificial Intelligence

A new framework utilizing Digital Twin technology and Deep Reinforcement Learning (DRL) has been developed for optimizing full vehicle active suspensions. This approach addresses the limitations of traditional suspension systems by enabling real-time, data-driven adjustments to enhance vehicle comfort, safety, and stability under varying conditions.
The integration of DRL with Digital Twin technology represents a significant advancement in automotive engineering, allowing for the simultaneous optimization of physical components and control strategies. This could lead to improved vehicle performance and adaptability in diverse driving environments.
The application of DRL extends beyond automotive systems, as seen in various fields such as smart manufacturing and medical imaging. The ongoing exploration of DRL in dynamic algorithm configurations and adaptive systems highlights its versatility and potential to revolutionize multiple industries by enhancing efficiency and responsiveness.

— via World Pulse Now AI Editorial System

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