Macro fiber composites (MFC) have proven useful as multifunctional material actuators for implementation in intelligent aerospace structures; however, due to nonlinear behaviors such as hysteresis and creep, incorporating sensor feedback is necessary to achieve sufficient control. In our work we use an antagonistic MFC unimorph system to produce a smooth trailing edge deflection in a morphing airfoil. In the past, piezoelectric flex sensors have been used to provide position feedback for the airfoil trailing edge, but these sensors also suffer from hysteresis. To achieve accurate position measurements from the flex sensors, we use time-series machine learning to model the relationship between flex voltage output and the true deflection. After testing offline, a long short-term memory (LSTM) neural network is implemented for inference on the hardware system and compared to a traditional linear model. True deflection information is obtained through external laser measurements, providing a context for comparing the accuracy of the mentioned state inference methods. Additionally, the sensor models are used in conjunction with a PD feedback controller to determine performance when controlling the aileron. Another difficulty in MFC actuator application is the change in performance under loading. Therefore, we perform final performance comparisons in a wind tunnel to simulate a realistic environment for the airfoil. We find the presented methods improve state inference performance over the traditional linear method, allowing for more accurate tip control under the aerodynamic loading.