A Sensorless vector Control strategy for induction machine (IM) operating in variable speed systems is presented. The Sensorless Control is based on a reduced-order linear observer based on terminal voltage and current as input signals. An estimation algorithm based on this observer is proposed to compute speed. It is shown that the proposed Sensorless Control is more sensitive to the stator resistance than to the rotor resistance. In order to tune the observer and to compensate for the parameter variations and the uncertainties, a separate estimation of the stator resistance is introduced. The equations to estimate the stator resistance are derived from the machine differential equations. For certain operating regions of the machine, it is verified that the stator resistance can be accurately estimated regardless of wide stator resistance variation. It is shown that design and hardware implementation of this method is simpler than the previous works. The simulation and experimental results demonstrate the good performance of the proposed observer and estimation algorithm and of the overall indirect-field-oriented-Controlled system.

In this paper, a Sensorless flatness based Controller with nonlinear observer for non-ideal boost converter is proposed. Losses are modeled with a voltage source series with input and a current source parallel to output. By a one-loop Control structure with flatness property, both output voltage and inductor current is regulated. But, flatness based Control is a model based method and requires extra sensors for obtaining data of all system parameters. For reducing numbers of sensors, a nonlinear observer is used to estimate output current and input voltage. So, sensors of these two parameters are eliminated. Simulation and experimental results are given to validate the proposed Controller and robustness of proposed Controller to variation of system parameters is obvious. In addition, simulation results of proposed Controller are compared with a two-loop Controller including PI and energy Control

Vectorial Control of the speed of an induction motor is an advanced speed Control method, enabling us to Control torque, magnetic flux individually as that of DC motor. This paper pre3sents, the effecting factors dominating on the motor &the primarily vectorial Control, leading to an advanced model of Control in various &arbitrary co- ordinate systems. Finally a biased field Control method with/&without speed sensors, introduced & simulated.

Brushless DC (BLDC) motors are used in a wide range of applications due to their high efficiency and high power density. In this paper, Sensorless four-switch direct power Control (DPC) method with the sector to sector commutations ripple minimization for BLDC motor Control is proposed. The main features of the proposed DPC method are: (1) fast dynamic response (2) easy implementation (3) use of power feedback for motor Control that is much easy to implement (4) eliminating the torque dips during sector-to sector commutations. For Controlling the motor speed, a position Sensorless method is used enhancing drive reliability. For reference speed tracking, a PI Control is also designed and tuned based on imperialist competition algorithm (ICA) that reduces reference tracking error. The feasibility of the proposed Control method is developed and analyzed by MATLAB/SIMULINK® . Simulation results prove high performance exhibited by the proposed DPC strategy.

this paper presents finite Control set-model predictive Control (FCS-MPC) in the low and high speed range for the Control of brushless DC (BLDC) motor. In the proposed Control method, by removing the mechanical sensor and using the sliding mode observer (SMO), the electromotive force (BACK-EMF) as well as the speed and position of the rotor are accurately estimated. Then through fcs-mpc to improve speed Control performance and reduce chattering, by minimizes a cost function, predicts the future behavior of the  system variables. The method is simulated with MATLAB software and the output speed and back-emf obtained from the Control model Control method with a limited Control set are compared with the slip model observer method. The simulation results show that the Control method used improves the speed convergence accuracy and reduces the steady-state error and the steady-state fluctuations.

This paper describes a simple way to Control the Brush Less DC Motor (BLDCM) for electrical applications. To Control this machine it is generally required to measure the speed and position of rotor by using the sensor because the inverter phases, acting at any time, must be commutated depending on the rotor position. The position sensors make the motor system more complicated and mechanically unreliable. A method for the estimation of the speed and rotor position of a BLDCM is presented in this work. Fuzzy based Back EMF observer is employed to estimate the speed by using measurements of the stator line voltage and line current. Most existing Sensorless methods of the BLDC motor have low performance at transients or low speed range and occasionally require an additional circuit. To overcome this problem, the estimation of a back-EMF is carried out by fuzzy logic techniques to improve the performance of the system. This method proposes back-EMF observer based on fuzzy function approximation and the system state equation of the BLDC motor. The fuzzy logic technique is used to estimate the speed of the BLDC motor under variable and fixed condition of the back-EMF. Finally, the speed is Controlled by using Proportional-Integral (PI) Controller with the help of fuzzy based estimation of the speed and rotor position. Here, fuzzy based estimation of speed and rotor position which is also verified with the sensor, feeds input to the PI Controller which does really well for good performance.