## 6 Conclusions  
This paper addresses the conflict between ride comfort and stability in the hydropneumatic suspension system of auxiliary transport vehicles in complex underground mine environments. A multi-mode active control method based on road surface grade identification is proposed. Through theoretical modeling, algorithmic optimization, and simulation verification, the following conclusions are drawn:  
(1) A road surface grade identification method based on LSTM networks is proposed. Vehicle field tests demonstrate that this method achieves classification accuracies exceeding 80% on a mining truck road surface and a cement road surface, accurately classifying complex underground mine road surfaces and providing reliable support for suspension parameter optimization;  
(2) A multi-mode hydropneumatic suspension control strategy based on underground mine roads' surface grade identification is proposed. By combining real-time road surface grade identification, active suspension control, and parameter optimization, the suspension parameters can be dynamically adjusted according to the characteristics of unstructuredunderground mine road surfaces. The experimental results show that compared to traditional passive suspensions, the proposed method reduces the sprung mass's acceleration by 21.2%, 18.86%, and 17.44% on B-, D-, and F-grade roads, respectively. The suspension's dynamic deflection is reduced by 10.34%, 11.55%, and 12.99%, and the tires' dynamic load is reduced by 9.21%, 12.1%, and 14.11%, respectively. The results demonstrate that the HOA-FSMC active hydropneumatic suspension integrates the Sliding-Mode Controller's robustness and Fuzzy Control's flexibility, with parameters optimized through the HOA. The system adapts to varying road conditions, significantly enhancing ride comfort, stability, and safety. The study further highlights the critical role of control parameter optimization in active suspension systems and validates the applicability and effectiveness of the HOA in parameter optimization. This study constructs a \"Perception-Decision-Action\" closed-loop control framework, providing dual theoretical and technical support for advancing the intelligent operation of mine transportation equipment. The methodology is transferable to suspension optimization for specialized vehicles operating in unstructured environments, such as in field exploration and forest engineering. In subsequent research, studies could delve into the effects of different hydraulic fluids on the suspension's stiffness and damping characteristics. A digital-twin-based suspension lifespan prediction system could be developed, and materials for suspensions can be further developed [25]. Regarding vehicle modeling, a high-fidelity multi-body vehicle dynamic model will be established to study coupled control strategies for multi-hydropneumatic suspensions. The road identification module can employ a multi-source heterogeneous sensor fusion framework. This framework would integrate visible light camera data, LiDAR point cloud data, and inverse dynamic analysis to enhance road grade recognition accuracy under low-illumination conditions through weighted fusion. The parameter optimization process could incorporate global sensitivity analysis to quantify the weighting impacts of the sprung mass's acceleration, suspension's dynamic deflection, and tires' dynamic load. Future research could also integrate digital twin technology to enable the real-time adaptive optimization of the suspension system, advancing intelligent suspensions toward autonomous decision making. Author contributions: Conceptualization, J.Y. and K.C. ; methodology, J.Y. and K.C. ; software, J.Y. and K.C. ; validation, K.C., Z.J. and C.Z. ; formal analysis, J.Y., Z.J. and C.Z. ; investigation, K.C. ; resources, Z.D. ; data curation, T.Z. ; writing--original draft preparation, J.Y. ; writing--review and editing, K.C. ; visualization, Z.D. ; supervision, K.C. ; project administration, J.Y. All authors have read and agreed to the published version of the manuscript.