This paper presents an estimator-based speed sensorless field oriented control (FOC) method for induction machines, where the state estimator is based on a self-contained, non-linear model. This model characterizes both the electrical and the mechanical behaviors of the machine and describes them with seven state variables. The state variables are estimated from the measured stator currents and from the known stator voltages using the observer. An important aspect is that one of the state variables is the load torque and hence it is also estimated by the estimator. Using this feature, the applied estimator-based speed sensorless control algorithm may be operated adequately besides varying load torque. In this work, two different variants of the control algorithm are developed based on the extended and the unscented Kalman filters (EKF, UKF) as state estimators. Dynamic performance of these variants are tested and compared by experiments and simulations. Results show that the variants have comparable performance in general, but the UKF-based control provides better performance if a stochastically varying load disturbance is present.