Advancing automobile active suspension control: an empirical study on the soft actor-critic algorithm

This paper explores the enhancement of active suspension systems in vehicles, focusing on improving stability and passenger comfort under varying road conditions. We introduce the Soft Actor-Critic (SAC) algorithm, a sophisticated reinforcement learning method, to advance the efficiency and responsiveness of automobile active suspension systems. SAC is adeptly tailored to navigate the complex dynamics of vehicle suspension, offering an intelligent alternative to traditional control strategies. Our experimental results demonstrate the SAC algorithm's effectiveness, with significant improvements in ride quality and vehicle stability over passive suspension systems. The SAC-controlled active suspension achieves smoother vehicle body displacement and reduced oscillations, underscoring its superior performance. This research marks a notable contribution by implementing SAC in active suspension control, signifying a major step forward in intelligent vehicle control. It sets the foundation for future explorations that could extend SAC's application beyond automotive to other adaptive control systems. Potential future directions include integrating SAC with various vehicle control systems, leveraging advanced neural networks, and real-world testing, further establishing reinforcement learning as a key driver in automotive technology advancement.