###### Abstract
Currently, flux-oriented control of IM drives is extensively used in the applications which required high performance. Nonetheless, the drive performance is negatively affected when the parameters are varied. The performances of the controller and observer are immediately downgraded in the existence of these uncertainties. Notably, the behavior of the control system is highly attentive to the state estimator. In order to manage these uncertainties, the online parameter identification has to be considered. An adaptive or robust conception strategy can be used for designing either the estimator and/or the controller schemes. To this consideration, in [39; 40; 41], both the rotor and stator resistances are estimated online involving neural networks, two extended Kalman filters, and modified extended Kalman filter, respectively. Although in [42], only stator resistance is estimated using an extended Kalman filter. The majority of these studies have guaranteed that the resistances of the rotor and stator coils and also the mutual inductances are obtainable. In [39], the rotor and stator resistances are observed by using several designs based on two different neural networks which are established starting from the IM model, where the authors revealed that the speed estimation is very sensitive to the system disturbance, which must be filtered. In [43], the stator currents and rotor flux are estimated using a sliding mode observer (SMO) observer, operated by the mismatch between the observed and measured stator currents. Through analyzing the performance of these observers, the SMO has revealed the most appropriate performance in achieving a robust estimation versus different types of disturbances; however, the obtained estimations have suffered from the chattering phenomenon. Another category of observers has been dependent on the adaptive mechanism, and known by the name of reference adaptive model control strategies [44; 45; 46]. These observers have depended on extracting the rotor position and speed information from the error between two estimated values of rotor flux. The problems associated with these observers are mainly the high sensitivity to the parameters variation, which deteriorates the estimation accuracy especially at very low speed range. Attemptshave been made to compensate the effect of parameters variation through developing a mechanism for estimating the varied parameters as in [45; 46], but this led to increasing the system complexity. Up to this review, and in order to avoid the previous deficiencies, the singular perturbation theory is employed and combined with the traditional indirect rotor-field oriented control (IRFOC) technique in order to achieve better system dynamics with parametric uncertainties. This has been achieved through formulating the state observation process in two different stages. First, a simultaneous observation of both rotor flux and rotor speed is adopted and secondly the rotor resistance observation is integrated to enhance the robustness of the sensorless scheme. The contributions of the intended sensorless observation mechanism can be summarized as follows. 1. A novel singularly perturbed sliding mode observer is proposed for a five-phase induction motor drive for estimating the speed and rotor resistance. 2. The design procedure of the proposed SMO observer is presented and explained in a detailed way which contributes in clarifying the base principles upon which the observer depends. 3. Load torque estimation is not required, which simplifies the observer construction. 4. State estimation procedure does not involve mechanical equations. Therefore, the proposed scheme does not use nonlinear estimation equations. 5. The rotor resistance and speed are estimated simultaneously, so that the effect of parametric variation can be minimized. 6. The singular perturbation theory used in this paper essentially works on two-time-scale system with slow and fast subsystems which simplifies the control system design and the structural analysis as well.