Semi-Active Control of a Two-Phase Fluid Strut Suspension via Deep Reinforcement Learning

Gas–oil emulsion struts (GOESs), with their simplified and low-cost design and minimal friction, offer attractive potential for industrial applications. However, they exhibit highly nonlinear damping behavior due to the compressibility of the gas–oil emulsion. This study proposes a semi-active control strategy for modulating the emulsion flow via a dynamically controlled solenoid valve. The GOES is modeled considering pressure-dependent friction and flow characteristics. A reinforcement learning model is further developed to modulate the opening area of the control valve under random road excitations to enhance vibration ride comfort, using a quarter-vehicle model framework. The validated model is used to analyze the strut’s performance under three different scenarios, namely, the original passive, optimal passive, and semi-active. The results suggest that the proposed semi-active strategy could yield a considerably lower root mean square of the sprung mass acceleration for both the passive and optimal systems. It is further shown that real-time adjustment of the control valve could yield nearly 27.2% enhancement in ride comfort performance in comparison to optimal passive GOES.