## 4 Conclusions  
This paper presents a design method for a robust static output feedback control for a MR damper-based semi-active suspension system, which adapts the MR damper current based solely on the information received from suspension deflection sensors.  
In consideration of the existing literature, an enhanced Bingham mathematical model has been formulated to characterize the mechanical response of the MR damper in relation to the magnitude of the current applied to it. A series of experimental tests were conducted to validate the model utilized in this study. A high overall correlation between experimental and predicted forces of \(R^{2}=0.995\) was found.  
Considering that not every vehicle state can be measured, convex LMI conditions were obtained to design practical robust MR damper controllers that depend only on the information received from suspension deflection sensors. The Lyapunov stability theory is employed to ensure the stability of the vehicle suspension system. A practical implementation of the proposed methodology in real time is guaranteed, as no supplementary estimation stages are required for those states that cannot be measured.  
The performance of the proposed system was evaluated under two distinct conditions: a road bump and a Class A road profile, in accordance with the ISO 8608 standard. It has been demonstrated that the incorporation of the proposed semi-active suspension system, as a substitute for a traditional passive suspension, can enhance ride comfort and safety in vehicles. In this case, vehicle vertical acceleration and suspension deflection are reduced by 50% and 73%, respectively, in the most unfavorable scenarios.  
Given the inherent theoretical mechanical advantage of active suspensions over semi-active suspensions, as they have less limitations on the force they can provide, at the expense of high energy consumption and cost, a viability analysis will be conducted as part of future works. This study will assist in identifying optimal application scenarios, balancing performance, energy efficiency, and cost-effectiveness, thereby guiding decision-making on the appropriate suspension type for various operational contexts.  
Conceptualization, F.V.-M., M.M.-U., M.J.-S., and B.L.B.; methodology, F.V.-M., M.M.-U., M.J.-S., and B.L.B.; software, F.V.-M.; validation, F.V.-M., M.M.-U., and M.J.-S.; formal analysis, F.V.-M.; investigation, F.V.-M., M.M.-U., and M.J.-S.; resources, F.V.-M., M.M.-U., M.J.-S., and B.L.B.; data curation, F.V.-M.; writing--original draft preparation, F.V.-M., M.M.-U., M.J.-S., and B.L.B.; writing--review and editing, F.V.-M., M.M.-U., M.J.-S., and B.L.B.; visualization, F.V.-M.; supervision, B.L.B.; project administration, B.L.B.; funding acquisition, F.V.-M. and B.L.B.All authors have read and agreed to the published version of the manuscript.  
Grant [PID2022-1364680B-100] funded by MCIN/AEI/10.13039/501100011033 and, by \"ERDF A way of making Europe\".  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LMI & Linear Matrix Inequality \\ LVDT & Linear Variable Differential Transformer \\ MR & Magnetorheological \\ NDTL & Normalized Dynamic Tire Load \\ PSD & Power Spectral Density \\ RMS & Root Mean Square \\ \end{tabular}