Delay-resilient robust control of automobile active suspensions via deep reinforcement learning

Key Points

• Improved robustness enhances the control systems of active suspensions—leading to better vehicle stability.
• Key evidence indicates that delay-resilient methods achieve a performance increase of 30% in stability measures.
• The approach utilizes deep reinforcement learning to optimize active suspension control under varying conditions.
• These findings support the application of advanced algorithms in automotive systems, opening avenues for future enhancements.