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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 study presents an improved sliding mode model reference adaptive system (SM-MRAS) speed observer for the fuzzy control of direct-drive wind power generation system with a permanent magnet synchronous generator (PMSG). The SM-MRAS speed sensorless observer is described and the corresponding algorithm is derived. The designed fuzzy controller is compared with the conventional PI controller by simulations and experiments. A dc motor is controlled to simulate the wind turbine and an active machine-side converter with space vector pulse width modulation control is adopted to realise the maximum power extraction. A 250-W PMSG experimental platform is built and the experiment results verify the validity of the proposed SM-MRAS speed observer.

An induction motor is generally used in industrial applications because it is reliable, robust, and low cost. Reliability is one of the essential parameters based on which the motor is selected, and the induction motor primarily comes into force. The well-founded induction motor gives good results under various operating states. To achieve this, the values of the motor are kept in mind. Dynamic simulation plays a significant part in evaluating the model’s design process to eliminate design errors in typical construction types and when testing the motor drive system. The induction motor is modeled in a synchronously revolving rotor flux-oriented frame, which is used as a reference. For sensorless vector control and induction motor control methods, accurate knowledge of a few induction motor parameters is necessary. The presentation of the drive will degrade if the original data in the motor do not match the values utilized in the controller. Various mechanisms have been developed to calculate the online and offline parameters of the induction machine for its application in high-performance drives. The foremost grail of this review paper is to present dynamic modeling and other considerable approaches used for estimating the induction motor parameter. This paper also constitutes some simulation examples related to dynamic modeling and parameter estimation techniques, which may be useful for specialists in the field of electric drive control systems.

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.

To improve the accuracy of the sensorless control system for permanent magnet synchronous motors, this paper proposes the based on fractional order integral sliding mode theory in the model reference adaptive system observer. The purpose of this approach is to enhance the robustness of the system. Furthermore, this paper introduces the second-order sliding mode reaching law to suppress chattering phenomena and improve the control accuracy of the observer. Simulation results show that this strategy achieves excellent dynamic and static performance.

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

Surface-mounted permanent magnet synchronous motor (SPMSM) is widely used in the industrial field with excellent performance, and the rise of artificial intelligence has also promoted its development. In order to improve the estimation accuracy and response of speed and position angle in the SPMSM sensorless control system, a novel sliding mode control model reference adaptive system (NSMC-MRAS) observer based on variable step hill climbing method for online optimization is proposed. Firstly, an MRAS adaptive observer was constructed, and a conventional SMC controller is used instead of the PI regulator to improve the robustness of the SPMSM parameters. Aiming at the problem of chattering caused by the sign function sgn(s), an improved NSMC-MRAS sensorless control strategy using the continuous function sigmoid(s) is proposed, which effectively suppresses the chattering phenomenon. To address the position estimation errors caused by non ideal factors such as control delay, filtering phase shift, and parameter deviation, the artificial intelligence variable step hill climbing method is used for online optimization and adaptive compensation. The experimental results show that the proposed NSMC-MRAS sensorless control strategy based on variable step hill climbing method for online optimization can quickly and accurately estimate the speed and position angle of SPMSM, improved the control performance and intelligence level of the system.

This paper presents a novel fuzzy-augmented model reference adaptive voltage regulation strategy for the DC–DC buck converters to enhance their resilience against random input variations and load-step transients. The ubiquitous proportional-integral-derivative (PID) controller is employed as the baseline scheme, whose gains are tuned offline via a pre-calibrated linear-quadratic optimization scheme. However, owing to the inefficacy of the fixed-gain PID controller against parametric disturbances, it is retrofitted with a model reference adaptive controller that uses Lyapunov gain adaptation law for the online modification of PID gains. The adaptive controller is also augmented with an auxiliary fuzzy self-regulation system that acts as a superior regulator to dynamically update the adaptation rates of the Lyapunov gain adaptation law as a nonlinear function of the system’s classical error and its normalized acceleration. The proposed fuzzy system utilizes the knowledge of the system’s relative rate to execute better self-regulation of the adaptation rates, which in turn, flexibly steers the adaptability and response speed of the controller as the error conditions change. The propositions above are validated by performing tailored hardware experiments on a low-power DC–DC buck converter prototype. The experimental results validate the improved reference tracking and disturbance rejection ability of the proposed control law compared to the fixed PID controller.

This paper introduces a new solution for the speed and current sensor fault-tolerant direct field-oriented control of induction motor drives. Two self-adjusting observers derived from a modified current-based model reference adaptive system (CB-MRAS) are presented. Finally, the recursive least squares method was used to estimate the parameters of the used observers. The method, in the proposed solution, provides a very fast and accurate finding of the observer parameters while maintaining relative simplicity and ease of implementation. The presented algorithm eliminates the CB-MRAS observer dependence on the induction motor parameters and also compensates for the inaccuracies in the evaluation of the stator voltage vector. The proposed fault-tolerant control offers the drive operation while either a speed sensor or one/two current sensors fault occurs. The drive still works with the direct field-oriented control even when no current sensors are healthy. The proposed scheme was simulated in the MATLAB/Simulink software environment. Then the algorithm was implemented in a floating-point digital signal controller (DSC) TMS320F28335 and tested on an induction motor drive prototype of rated power of 2.2 kW to validate the proposed schemes.

The use of direct torque control (DTC) for electric motor applications provides many benefits, such as quick dynamic response and efficient operation at low speeds. However, it does come with some disadvantages (i.e., torque ripple and an additional expense with speed sensors) that could complicate the system and add to the overall costs associated with the system. Here, we present an approach that uses an innovative method to eliminate the need for speed measurement to employ DTC on an electric pump driven by an induction machine using an asynchronous squirrel cage method. We achieved this through utilizing a model reference adaptive system (MRAS) in conjunction with artificial neural networks (ANN). Using MRAS enabled real-time measurements of motor speed, reducing complexity in the motor model and improving the computational load. However, adding MRAS to DTC does introduce some level of estimation error and incurs additional torque ripples; we have incorporated the use of an ANN to assist in optimizing the switching schemes that mitigate these adverse effects. In our experimental study, we utilized MATLAB/Simulink for rigorous testing and validated our results on a dSPACE 1104 board. The improvements in response time from 0.8 to 0.58 s (DTC to DTC-MRAS + ANN) and a reduction in total harmonic distortion (total harmonic distortion reduced from 9,76% to 7,80%) provide an innovative new perspective to the current literature, demonstrating both the repeatability and effectiveness of this approach while producing significant improvements in control methods compared with both DTC-MRAS and DTC-ANN techniques by numerous control indices.