A Magnetorheological damper is a commonly used component in semi-active suspensions that achieves a high force capacity and better performance than a passive system, without the added expense and power draw of a fully active system, all while maintaining failsafe performance. To fully exploit the capabilities of an MR Damper, a high fidelity controller is required that is simple and easy to implement, yet does not compromise the accuracy or precision needed in many high-performance applications. There is a growing need for this level of operation, and this proposed work addresses these requirements by creating an empirically derived invertible model that enables the development of more accurate command signals by capturing the effect of temperature on a MR Damper's performance capabilities. Furthermore, this solution is specifically tailored for real-time application and does not require force feedback. Thus it requires low computation power and minimizes end-user cost by eliminating the need for additional high cost sensors such as load cells. A notable observation that resulted from the development of this proposed model was the difference in behavior between on and off states. Additionally a unique behavior was recognized with respect to the transition between high speed and low speed damping. For validation, the proposed model was compared against experimental data as well as an industry standard Spencer model; it produced excellent results in both cases with minimal error. / Master of Science