Hybrid reinforcement learning-model predictive control framework for full-band (0–500 Hz) vibro-acoustic optimization in active suspension systems

Key Points

• Vibro-acoustic optimization improves system performance, enhancing overall ride quality and comfort.
• The framework combines reinforcement learning and model predictive control to adaptively manage parameters.
• Analysis employs a hybrid approach across various frequency ranges, focusing on 0–500 Hz for effective performance.
• This optimization method highlights potential improvements in vehicle dynamics and noise reduction.