## 1 Introduction  
In recent years, power electronics and modern control designs are emerging widely which boost the use of Permanent Magnet Synchronous Motors (PMSM). The PMSM is extensively used in various fields due to multi-facet benefits, such as high dynamic speed, high torque, good efficiency, low maintenance cost, reliable operation, and simple structure. In motors, to achieve perfect commutation, signals after every 60 electrical degrees need to be computed to estimate position and speed. For this purpose, sensors on the rotors are used, such as photovoltaic and hall sensors [1]. However, sensors require additional space, leading to an overall increase in size and cost with frequent maintenance. Therefore, to eliminate such problems, sensorless control technology suddenly received great attention from researchers [2]. In PMSM, for sensorless control, observers are designed to estimate such states/parameters that cannot be measured directly or through other available states. There are many techniques available for estimating the motor parameters to control the motor. Problems associated with estimation techniques without sensors include inaccuracy at low speed,disturbance rejection, poor dynamic performance, constraint handling with safer starting conditions, and switching losses of the inverter due to high switching frequency [3]. In PMSM, the sensorless estimation techniques are categorized as: (a) adaptive methods, (b) non-adaptive methods, (c) signal injection methods, and (d) artificial intelligence methods [4; 5; 6]. The non-adaptive methods include stator current and stator voltage estimators, flux-based estimators, back EMF estimators, and DC link methods [7]. Moreover, there are several sub-groupings of adaptive methods, such as Luenberger-based observers, Model Reference Adaptive (MRAS) estimators, Kalman-based stochastic filters, and sliding mode observers [8]. All these techniques are mature enough to achieve the maximum optimal results. Table 1 illustrates the limitations and benefits of all sensorless estimation techniques. The Kalman Filter (KF) is an optimal state estimator but only for linear systems because the update rule of the KF makes optimal decisions based only on the first two instants of states viz. mean and covariance. Therefore, the cases where the system is nonlinear, the KF does not provide optimal results because of non-Gaussian noise. However, a modified form of the KF is the Extended Kalman Filter (EKF), in which transformation from nonlinear to linear is done via the Jacobian matrix that makes the calculations simpler but less accurate. In the case of highly nonlinear systems, linearization results in bad performance [15]. Approximation issues of the EKF are addressed in the Unscented Kalman Filter (UKF) formulation. The Unscented Transformation (UT) is used in the UKF process instead of linearization approach. Gaussian Random Variable (GRV) represents the state distribution of both the algorithms; however, in UKF minimum sample points are carefully chosen. The UT is used to calculate mean and covariance, which further helps find sigma points as accuracy is proportional to the number of sigma points [16]. The sigma points propagated through the true nonlinear function captures the true mean and covariance of the GRV. This process accurately estimates the posterior mean and covariance for any nonlinearity up to 2 nd order. The Model Predictive Control (MPC) is one of the widely used predictive control methods. In MPC, the optimal control and control instructions are obtained by a specific objective function that is determined through the mathematical model of the controlled object. In MPC, the cost function, system nonlinearities, and constraints can easily be