The goal of this study was to figure out how to regulate an induction motor in a hybrid electric vehicle. Conventional combined vector and direct control induction motors take advantage of the advantages of vector control and direct torque control. It is also a method that avoids some of the difficulties in implementing both of the two control methods. However, for this method of control, the statoric current has a great wealth of harmonic components which, unfortunately, results in a strong undulation of the torque regardless of the region speed. To solve this problem, a five-level neutral point clamped inverter was used. Through multilevel inverter operation, the voltage is closer to the sine wave. The speed and torque are then successfully controlled with a lower level of ripple in the torque response which improves system performance. The analysis of this study was verified with simulation in the MATLAB/Simulink interface. The simulation results demonstrate the high performance of this control strategy.

This paper offers a system for an electric vehicle. It consists of digitally controlling an induction motor without using a speed sensor. The machine is powered by a five-level cascading H-bridge inverter. The SVM control principle is used to manage the status of the five-level inverter; this removes harmonics. The H-bridge inverter converter is powered by photovoltaic sources via a serial converter, using the maximum power point tracker control principle. This structure can also reduce shading losses. In the absence of a mechanical sensor, a dynamic model of the asynchronous machine is utilized with the state variables defined in the stator reference frame. The state vector consists of the components of the rotor flux and stator current. The article provides a comparison of two methods widely used on an induction motor drive. The adaptive model-reference system method and Luenberger observer are evaluated using an active control strategy to reject disturbances to minimize the impact of disturbances. The operating principles of each method are described, and the mathematical models of training systems are developed. Both methods provide a promise for high-speed estimate applications in simulation environments. The simulation results obtained show the correct operation of both observers. Perfect decoupling between the velocity and flow control loops is observed, taking into account any disturbances that may affect the system.

In this paper, a robust vector control sensorless based on three Active Disturbance Rejection Controllers (ADRC) for an induction motor fed by a five-level cascaded H-bridge was developed. The estimation of rotor position is achieved using a Luenberger estimator. The proposed model considers the disturbances and the inaccuracies of the system and compensates them using the extended state observer of ADRC. Moreover, a five-level cascaded H-bridge inverter is the best topology to minimize the torque ripples and to improve the current output of IMunder control. The suggested control system was simulated under different conditions using MATLAB-Simulink. The results demonstrate the effectiveness and the robustness of the proposed system against disturbances such as variations of parameters and load torque.

This paper proposes the analysis and implementation of a nonlinear backstepping controller to regulate the current in Grid-tied five-level inverter system via an LCL filter. Designing a robust regulator for nonlinear systems is often a challenging task in control theory. The study addresses the challenges of enhancing the performance of the conversion system by selecting appropriate regulated variables and ensures synchronization between the signals of the grid and the five-level inverter. Additionally, it includes a grid current control using the backstepping control method. This method is based on the Lyapunov stability approach, involves switching a five-level inverter with high-frequency modulation, and employs Sinusoidal Pulse Width Modulation as the selected modulation strategy. The proposed system configuration and the implementation of the backstepping control method are discussed step-by-step to ensure the efficiency of the system analysis. Simulation results in the MATLAB/Simulink environment validate the theoretical analysis. The nonlinear backstepping controller demonstrates excellent performance in tracking reference signals and responds to abrupt changes in the reference, highlighting its effectiveness for current control in grid-connected inverter systems.

This paper, present a work on indirect field oriented control of an asynchronous machine fed by a multi-level inverter with using adjustment mechanisms for estimating the rotation speed, the stator resistance and the rotor resistance. In the absence of the speed sensor, we use a MRAS (Model reference adaptive system) observer to estimate the mechanical speed. In the MRAS method, the magnetizing current of the adaptive model is compared with that of the reference model. The rotor speed, the stator and rotor resistances are estimated from the magnetizing currents difference of the two models using adequate adaptive mechanism. The choice of this Observer is due to its simplicity, less computation time and good stability. For the choice of multi-level converter, we use five-level cascaded H-bridge inverters to feed the induction motor. This multi-level converter allows to obtain a greater number of output voltage levels with a very acceptable harmonic spectrum. The present approach (IFOC, MCHI and MRAS) helps to achieve a good dynamic response at low speed with reduction of total harmonic distortion. Simulation results show the effectiveness of the proposed method with consideration of variation in thermal parameters.

This paper outlines the design of a predictive controller combined with an active disturbance rejection control (ADRC)-type controller to enhance the dynamic performance of the induction motor powered by a nine-level converter. The predictive control law proposed is derived from the Poisson Laguerre model, based on predictive control. The motor is controlled using indirect field-oriented control, both with and without a speed sensor. For speed estimation, the Luenberger observer of order 4 is used. The predictive method utilized allows for the dynamic adjustment of control parameters based on those of the induction motor. The paper aims to eliminate internal and external disturbances and reduce total harmonic distortion (THD) through the use of a Nine-level cascaded H-bridge inverter. Space vector pulse width modulation (SVPWM) signals are generated using the logic of hexagon decomposition. The SVPWM method operates by decomposing higher-level hexagons into multiple two-level hexagons. The Poisson-Laguerre model (PLM) is also compared with the ADRC and the proportional-integral (PI) control. Simulations with MATLAB/SIMULINK software for an induction motor are performed to test the performance of each controller and the validity of the observer.