## 1 Introduction  
As a core piece of equipment for tunnel transportation in underground mines, auxiliary transport vehicles can replace manual labor for material transport tasks, offering advantages such as high efficiency, strong flexibility, and enhanced safety. However, subsurface conditions are highly dynamic, and unstructured roads exhibit significant randomness, leading to excessive sprung mass acceleration and increased tire dynamic load fluctuations, which severely affect the stability of the vehicle's operation. The hydropneumatic suspension system, serving as the core load-transmitting component between the vehicle's chassis and axle, is responsible for transmitting load and torque, as well as effectively dampening shocks from uneven road surfaces. However, relying solely on traditional passive hydropneumatic suspension systems is insufficient to adapt to the strong randomness of unstructured roads in underground mines. Therefore, the hydropneumatic suspension system must incorporate real-time road surface identification and active control functionality to effectively adjust the suspension under varying road conditions, reduce vibrations, and enhance the vehicle's ride comfort and adaptability. Auxiliary transport vehicles tweak their active hydropneumatic suspension settings to adapt to various road conditions. To obtain the right suspension, it is necessary to pinpoint the road's roughness level. Techniques for recognizing this grade of roughness fall into three main categories: those that rely on physical contact, those that do not, and those that gauge the vehicle's system response. Contact-based measurement [1; 2] uses mechanical probes to directly contact the road surface. This technique ensures a high level of precision for slow-speed detection, though its capabilities are constrained by the measurement gear, and it lacks real-time road data updating. Non-contact-based measurement [3; 4] uses sensors, such as LiDAR and cameras, to remotely scan and identify road surfaces in real time. However, the accuracy of LiDAR or visual sensors decreases in dimly lit and dusty environments, which are common in mining areas, affecting the precision of the results. Vehicles' system-response-based methods identify the road surface indirectly by analyzing vehicles' responses obtained from onboard sensors. These methods are highly adaptable and beneficial for the development of controllable suspension systems. [PERSON] et al. [5] introduced a novel method for identifying road surface gradients. Their approach utilizes Support Vector Machines (SVMs) to dissect the behavior of an all-terrain crane under a variety of typical road conditions. Using hydraulic pressure and displacement data for training, this method achieved an accuracy rate of 98%. [PERSON] et al. [6] introduced an adaptive Kalman filtering method that classifies road surfaces based on vertical acceleration, optimizing the estimation accuracy by combining noise variances. Active suspension control systems enhance vehicles' ride comfort and stability by equipping active actuators that generate the optimal control forces based on input signals. [PERSON] et al. [7] developed a quarter-vehicle suspension model by applying the Lagrange-Euler approach and then fine-tuned the membership function parameters for both fuzzy-logic-based control and fuzzy-logic-based LQR control through particle swarm optimization. [PERSON] et al. [8] suggested a semi-active hydropneumatic suspension control method, leveraging backpropagation and active disturbance rejection techniques. [PERSON] et al. [9] introduced a genetic algorithm for optimizing the fuzzy-logic-based PID control strategy for air suspension. Recently, combining sensors for high-precision road profile estimation with intelligent algorithms to optimize control strategies has become a significant direction for suspension system research. By accurately identifying road conditions, the suspension system can adjust its control strategy dynamically to optimize the vehicle's dynamic performance. [PERSON] et al.