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In this study, a direct torque and flux control is described for a six-phase asymmetrical speed and voltage sensorless induction machine (IM) drive, based on non-linear backstepping control approach. First, the decoupled torque and flux controllers are developed based on Lyapunov theory, using the machine two axis equations in the stationary reference frame. In this control scheme, the actual stator voltages are determined from dc-link voltage using the switching pattern of the space vector pulse-width modulation inverter. Then, for a given motor load torque and rotor speed, a so-called fast search method is used to maximise the motor efficiency. According to this method, the rotor reference flux is decreased in the small steps, until the average of real input power to the motor reaches to a minimum value. In addition, a model reference adaptive system-based observer is employed for online estimating of the rotor speed. Finally, the feasibility of the proposed control scheme is verified by simulation and experimental results.

In order to achieve high performance of sensorless direct torque controlled induction motor drive at medium and low speed regions in case of Gaussian-noised stator currents, extended Kalman filter is utilized. At first, sensorless control using rotor-flux-based model reference adaptive system is described. Then, extended Kalman filtering that uses full state-space model of the induction motor is employed to obtain estimated stator currents for the sensorless control. Unmeasured rotor fluxes in extended Kalman filtering are computed based on their relationship to estimated stator fluxes and measured stator currents. The estimated stator currents are utilized to compute input quantities for direct torque control. Simulations are deployed in case of both process and measurement noises of stator currents. Performance comparisons based on two indices: normalized integral of time multiplied by absolute value of speed difference and maximum value of absolute value of relative speed difference between two sensorless control methods with and without extended Kalman filter, are carried out. Through simulations in Simulink environment of Matlab software, theoretical assumptions are confirmed by the fact that the evaluation indices of the proposed method are decreased by at most 75% and 80% compared to the method without extended Kalman filter.

Induction motor with indirect field oriented control is preferred for high performance applications due to its excellent dynamic behavior. However, it is sensitive to variations in rotor time constant, especially variation in rotor resistance which needs to be estimated online. Conventionally the model reference adaptive system with fuzzy logic controllers as adaptation is used, which works satisfactorily for one particular operating condition and fails under variable operating condition. Therefore the need arises for a fuzzy controller whose parameters are tuned using evolutionary algorithm. In this paper, the input/output gain and the membership function parameters of the fuzzy system are optimized using genetic algorithm and particle swarm optimization to obtain an optimal designed fuzzy controller for rotor resistance estimation. The system is investigated in MATLAB/Simulink environment. Results shows that the steady state error in estimation of rotor resistance by the proposed controller under stringent operating condition is better with the proposed controller as compared to the conventional trial and error based fuzzy controller.

The performance of a stator current-based model reference adaptive systems (MRAS) speed estimator for sensorless induction motor drives is investigated in this study. The measured stator currents are used as a reference model for the MRAS observer to avoid the use of a pure integrator. A two-layer, online-trained neural network stator current observer is used as the adaptive model for the MRAS estimator which requires the rotor flux information. This can be obtained from the voltage or current models, but instability and dc drift can downgrade the overall observer performance. To overcome these problems of rotor flux estimation, an off-line trained multilayer feed-forward neural network is proposed here as a rotor flux observer. Hence, two networks are employed: the first is online trained for stator current estimation and the second is off-line trained for rotor flux estimation. Sensorless operation for the proposed MRAS scheme using current model and neural network rotor flux observers are investigated based on a set of experimental tests in the low-speed region. Using a neural network rotor flux observer to replace the current model is shown to solve the stability problem in the low-speed regenerating mode of operation.

This paper presents a sensorless control strategy for a six-phase induction generator (6PIG) operating at low speed (125 rpm). The proposed approach is based on the Model Reference Adaptive System (MRAS), with an initial estimation scheme developed using the reference model as the rotor flux. Simulation studies were conducted in MATLAB/Simulink 24.2.0.2740171 (R2024b) Update 1 and experimentally validated on a 24 kW–125 rpm 6PIG, to demonstrate the feasibility and performance of this method. A reactive power-based MRAS variant was also proposed to overcome the observed limitations. Comparative analysis showed a significant improvement in estimation accuracy and dynamic response compared with the flux-based MRAS. Robustness tests under fault conditions, such as opening phases, confirmed that the reactive power-based MRAS maintains a stable and accurate rotor speed estimation. These findings demonstrate the potential of reactive-power-based MRAS for the sensorless control of six-phase induction generators (6PIGs) in renewable energy systems.

In this paper, we propose the application of a speed estimation strategy to a fuzzy logic control flux estimator for a sensorless adaptive rotor flux direct-vector-controlled (RFDVC) induction motor drive. The RFDVC induction motor drive was established using the stator current and rotor flux, with the stator current being obtained from the induction motor. The model reference adaptive system (MRAS) theory was utilized to develop an adaptive rotor flux estimator based on voltage-model and current-model flux estimators. The estimated rotor speed and synchronous angle position are derived from the adaptive flux estimator. The adjustment mechanism of this estimator was designed using the fuzzy logic control strategy because this scheme is simple, easy to implement, and requires no precise information about the mathematical model. The MATLAB/Simulink® toolbox was used to simulate this system, and all the control algorithms were realized using a TI 6713-and-F2812 DSP card to validate this approach. Both the simulation and experimental results (including the estimated rotor speed, electromagnetic torque, and stator flux locus) confirmed the effectiveness of the proposed system and thereby validate the proposed approach.

By using the direct torque control (DTC), robust response in ac drives can be produced. Ripples of currents, torque and flux are oberved in steady state. space vector modulation (SVM) applied in DTC and used for a sensorless induction motor (IM) with fuzzy sliding mode speed controller (FSMSC) is studied in this paper. This control can minimize the torque, flux, current and speed pulsations in steady state. To estimate the rotor speed and stator flux the model reference adaptive system (MRAS) is used that is designed from identified voltages and currents. The FSMSC is used to enhance the efficiency and the robustness of the presented system. The DTC transient advantage are maintained, while better quality steady-state performance is produced in sensorless implementation for a wide speed range. The drive system performances have been checked by using Matlab Simultaion, and successful results have been obtained. It is deduced that the proposed control system produces better results than the classical DTC.

The study discusses the problem of speed and flux estimation for the induction motor (IM) drive and presents the design of two sliding mode observers (SMO) with compound manifolds. Both observers are developed using the IM model in the stationary reference frame. The first observer is a single-manifold SMO – it estimates the motor fluxes and yields an approximate value of the speed; however, it is not a converging observer. The single-manifold design is transformed into a double-manifold observer by adding extra feedback terms – this leads to a fully convergent observer that also yields an accurate estimate of the speed. The observers are designed using compound manifolds, which are chosen as a combination of the estimated fluxes and current mismatches. Observers with compound manifolds have been rarely investigated because they cannot be designed using a standard procedure; however, they are shown to have interesting properties. Observer uniqueness is also discussed. The methods proposed are suited to a sensorless IM drive control algorithm where the speed, the flux magnitude and the rotor flux angle are needed. The theoretical developments are supported with simulations and experiments.

This work presents a novel direct torque and flux control (DTFC) of permanent magnet synchronous motor (PMSM) with analytically-tuned proportional integral (PI) controllers. The proportional (K_p) and integral (K_i) gains of the PI controllers were accurately determined, from first principle, using the model of the control system. The PI flux and torque controllers were then developed in rotor reference frame. The designed PI controllers, together with the torque and flux controllers, were tested on a permanent magnet synchronous motor (PMSM). The results obtained were compared with results from conventional DTFC system using manually-tuned PI controllers. The total harmonic distortion (THD) of motor phase currents is 18.80% and 4.81% for the conventional and proposed models respectively. This confirms a significant reduction in torque ripples. The control system was tested for step torque loading and found to offer excellent performance both during load changes, speed reversal, and constant load conditions.

This paper presents a different technique for the online stator resistance estimation using a particle swarm optimization (PSO) based algorithm for rotor flux oriented control schemes of induction motor drives without a rotor speed sensor. First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts. Finally, a novel method for the stator resistance estimation based on the PSO algorithm is presented for the two MRAS-type observers. Simulation results in the Matlab/Simulink environment show good adaptability of the proposed estimation model while the stator resistance is varied to 200% of the nominal value. The results also confirm more accurate stator resistance and rotor speed estimation in comparison with the conventional technique.