A robust optimal framework is designed herein to mitigate the oscillatory dynamics in a doubly fed induction generator (DFIG) even in the presence of network disturbances and input variation. To address uncertain dynamics, herein, a novel transformation formula is developed for a wind energy conversion system. An unscented Kalman filter is applied to estimate the unmeasured internal states of the wind energy conversion system using terminal measurements. The detailed convergence and stability analyses of the presented framework are investigated to validate its effectiveness. Additionally, comparative modal analyses are carried out to demonstrate the improvement in the damping of critical low-frequency oscillatory modes using the presented framework. The simulation results demonstrate satisfactory performance under various operating scenarios, such as increasing and decreasing wind speed and varying the terminal voltage. The comparative performance is demonstrated to validate the effectiveness of the presented framework over that of the state-of-the-art frameworks.