Controlled Induction Motor Research Articles

New disturbance observer-based speed estimator for induction motor

<p>This paper discusses a novel disturbance observer designed as an estimator to determine the rotor speed of an induction motor. This observer is a solution to obtain a simple structure with a small number of compact observer gains. Furthermore, the adaptation law is no longer required to estimate induction motor speed values. This is a machine model-based computation method that uses a stationary reference frame. The nonlinearity problem is solved using an additional state vector in the observer model, which is known as an extended state observer. This approach easily and systematically determines the observer gain by applying the linear quadratic regulator (LQR) method, thereby avoiding time-consuming trial errors. The proposed observer, which was presented in both continuous and discrete forms, was tested using a sensorless V/f- controlled induction motor. The simulation results show that the proposed observer can accurately estimate all states, namely, the rotor flux and stator current; therefore, the proposed estimator provides the speed and electromagnetic torque for a wide operational range of speeds and load torques. It was also shown that the proposed observer was robust to noise and uncertainty in induction motor parameters.</p>

Integer PI, fractional PI and fractional PI data trained ANFIS speed controllers for indirect field oriented control of induction motor

Induction motor drives with variable speed applications that employ vector control are quite popular nowadays because they provide strong dynamic performance and flexible speed control. By decoupling the torque-producing current components of stator current from the rotor flux, Indirect Field Oriented Control is recognized for generating excellent performance in induction motor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers' performance in terms of load variation capabilities, motor parameter variation, and speed tracking. Consequently, a comparison of the three controllers is important to select which controller performs high in induction motor drive. Indirect Field Oriented Control of induction motor with Fractional Proportional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers are all discussed in this work along with their designs and comparative analysis. The square of error was used as a fitness function to genetically optimize the FPI and IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS) controller uses a hybrid learning approach. It is trained by the FPI controller's input-output data. Using the results of MATLAB simulations under various operating situations, the performance of the ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller includes an extra parameter for adjustment, namely integration order, it performed better than IPI controller for speed control of the induction motor. According to the simulation findings, the percentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495 %, 12.062 %, and 14.699 % respectively. As a result, ANFIS exhibits a drastic reduction in overshoot. Additionally, with the ANFIS controlled induction motor drive, the speed achieves the required set value at 0.14 s. For no load, constant, and changing loads, the induction motor drive's performance has been examined.

Direct torque control of induction motor based on double-power-super-twisting sliding mode speed control for electric vehicle applications

Direct torque control of induction motor based on double-power-super-twisting sliding mode speed control for electric vehicle applications

Implementation and Stability Analysis of an Improved V/f Induction Motor Control Scheme Based on Constant Rotor Flux

Implementation and Stability Analysis of an Improved <i>V/f</i> Induction Motor Control Scheme Based on Constant Rotor Flux

Development of IGBT Driver Module for Control of Launcher Induction Motor

Development of IGBT Driver Module for Control of Launcher Induction Motor

Multi‐parameter identification method of induction motor based on coupling and small signal injection

AbstractSensorless induction motor control has been widely applied to the rail transit field. However, achieving a safe stop of a train using electric braking without applying air braking has been an urgent problem to be solved. The current research only considers the stability of the speed identification in the low‐speed region and does not consider the impact of inaccurate parameters on the stability, which cannot ensure the stable braking and parking of the train under all working conditions. To address this problem, the coupling relationship between the motor speed and the stator resistance is used and an adaptive rate of them is designed based on the Lyapunov stability design law. In addition, aiming to reduce the torque ripple, a torque ripple elimination link is designed to cancel the torque ripple caused by the small‐signal injection. Experiments show that the proposed parallel identification strategy of speed, stator resistance, and rotor resistance can ensure the system operation stability in the low‐ and zero‐speed regions without increasing the torque ripple.

Improving Robustness and Dynamic Performance of Sensor less Vector-Controlled IM Drives with ANFIS-Enhanced MRAS

The Model Reference Adaptive System (MRAS) enables effective speed control of sensorless Induction Motor (IM) drives at zero and very low speeds. This study aims to enhance the resilience and dynamic performance of MRAS by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. To address issues related to parameter uncertainties, load variations, and disturbances, the combination of MRAS and ANFIS is investigated. The ANFIS controller improves the dynamic performance by adapting its parameters based on the error between estimated and measured rotor speeds. This allows for better tracking of the reference speed and smoother drive operation. The proposed MRAS scheme with ANFIS reduces the sensitivity of the sensorless control system to parameter variations, such as changes in motor parameters or load torque, thereby enhancing stability. The primary goal of this system is to maintain stability and reduce the impact of parameter variations on the sensorless control system. The performance of the proposed system is evaluated using the MATLAB platform and compared with existing systems. Results indicate that the ANFIS-enhanced MRAS offers superior dynamic performance and robustness, making it a viable solution for applications requiring precise speed control and high reliability.

Speed Control of Sensorless Induction motor based on Grey Wolf Optimizer Fractional Order Controller using MRAS based Speed Estimation

Speed Control of Sensorless Induction motor based on Grey Wolf Optimizer Fractional Order Controller using MRAS based Speed Estimation

Loss Minimization Model Predictive Current Control for Linear Induction Motors

To further improve the operating efficiency of linear induction motor, this paper proposes a loss minimization model predictive current control method based on governable loss online calculation. First, the method derives the equivalent circuit of linear induction motor containing independent iron loss branches, and establishes the loss model containing iron loss. Second, the expressions of stator d‐axis current with governable losses and secondary flux amplitude are derived from theoretical analysis, and then a given value of stator d‐axis current is derived when the governable losses are minimal. Then, a chain observer is designed to observe the secondary flux and calculate the governable loss and stator d‐axis current under the current operation. Finally, a method is proposed to introduce the stator d‐axis current observation results into the model predictive current control, which ensures the loss suppression effect and improves the dynamic performance at the same time. In addition, this article also compares the loss minimization controllers with or without iron loss. The simulation results are consistent with the theory, and the optimal control of the secondary flux of the linear induction motor can be successfully realized, thus effectively reducing the energy loss of the motor in the dynamic operation process. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

An Advanced Diagnostic Approach for Broken Rotor Bar Detection and Classification in DTC Controlled Induction Motors by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT Methodology

This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC’s closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions.

Double-Closed-Loop Model Predictive Control Based on a Linear Induction Motor

The conventional PI control, which has been extensively employed in the high-performance control of linear induction motors, has the benefit of a straightforward concept, quick dynamic response, and simple control. Nevertheless, the linear induction motor is more susceptible to outside disturbances and mismatched parameters since it is a highly linked, high-order, nonlinear, time-varying, complex system. Because of this, this paper proposes a double-closed-loop control of the linear induction motor, whose speed and current loops are the model-predictive speed controller and the model-predictive current controller, respectively, to solve the problems of speed overshoot and oscillation when the linear induction motor is operated under PI control. The findings demonstrate the benefits of the suggested control strategy, which significantly enhances the linear induction motor’s speed control performance. These benefits include improved dynamic performance, limited overshooting, and robust resistance to load disturbance.

HYBRID TYPE-1 AND 2 FUZZY SLIDING MODE CONTROL OF THE INDUCTION MOTOR

This paper focuses on developing two innovative induction motor (IM) control techniques. These techniques are based on the hybridization of Lyapunov theory (sliding mode) and artificial intelligence (type 1 and type 2 fuzzy logic). We will then compare these two control techniques to determine which is more robust. This comparative analysis will be based on a series of tests that we have carried out, covering the system's transient and steady-state operations under identical conditions. The first test involves observing the simulation results obtained by applying these control techniques to the motor to control the generated mechanical power. This qualitative comparison enables these controls to be evaluated for and without the application of external variations. The second test quantifies the different control laws based on quantified measurements, highlighting the performance of each technique in terms of error and time. This test is called a quantitative comparison. Finally, the last examination involves altering the machine parameters, as these values naturally experience fluctuations caused by diverse physical phenomena like inductance saturation and heating of the resistors. This comparison enables the robustness of the control techniques to be assessed.

Adaptive field-oriented control of current-fed induction motors: a discrete-time implementation

Indirect field oriented control (IFOC) is the industry standard for high performance current-fed induction motors. It is well-known that this controller preserves stability in the face of large rotor resistance variations – which are inevitable in practical scenarios – but its performance is significantly degraded. To overcome this drawback many adaptation schemes have been reported, both in drives and control-oriented publications, with the overwhelming majority of these developments done in continuous-time. This, in spite of the fact that the IFOC, as well as their proposed adaptive versions, involve the solution of highly nonlinear differential equations, whose discretization is far from obvious. In this paper we try to reverse this trend and present a discrete-time estimator of the motor parameters and an adaptive IFOC based on it. It is shown that the proposed estimator and adaptive controller are globally convergent under very weak prior knowledge and excitation assumptions. Simulations, that validate the theoretical claims and illustrate the robustness of the schemes, are also presented.

A hybrid methodology based battery power management and torque control of open-end winding induction motor drive for electric vehicles

This paper proposes a hybrid strategy for an isolated battery-powered induction motor drive with two stages fed by an inverter and open-end winding, designed for electric vehicle applications. The hybrid approach, termed EOO-RERNN, combines the Eurasian Oystercatcher Optimizer (EOO) and Recalling Enhanced Neural Network methods. RERNN predicts the control pulses generated by EOO, which are utilized to control the voltage source inverter (VSI) switches. The system comprises dual motors, two battery packs (BPs), and VSIs supplying power to both the rear motor (RM) and front motor (FM). The primary objective of the EOO-RERNN technique is to regulate each DC-link's power share to maintain equilibrium in the electric vehicle's charge. Direct Torque Control (DTC) is employed due to its quick and precise torque response and tolerance for parameter variations. Proper tuning is required to control the state of charge battery level. The EOO-RERNN method is implemented in the Matrix Laboratory (MATLAB) platform, with performance contrasted to other methods such as Wild Horse Optimizer (WHO), Grasshopper Optimization Algorithm (GOA), and Heap-Based Optimizer (HBO). Simulation results indicate a motor speed of 4850 rpm for the EOO-RERNN method.

New Opportunities in Real-Time Diagnostics of Induction Machines

This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance.

Hysteresis Current Control for Six-Phase Induction Motor Drives With Reduced Ripple and Improved Tracking Based on Subspace Decomposition and Restrained Voltage Vectors

Hysteresis Current Control for Six-Phase Induction Motor Drives With Reduced Ripple and Improved Tracking Based on Subspace Decomposition and Restrained Voltage Vectors

Fault-Tolerant Technique Against Current Sensors for Model Predictive Control of Induction Motor Drives

Fault-Tolerant Technique Against Current Sensors for Model Predictive Control of Induction Motor Drives

ADRC-LSE Control of a Dual-Star Induction Motor Powered by a Dual 3-level NPC Inverter

In the context of electric vehicle propulsion drives, this paper presents anActive disturbances Rejection Control (ADRC) strategyapplied to a Dual Star Induction Motor (DSIM)powered by dual 3 levels inverter type NPC. The proposed method focuses on enhancing the Active Disturbance Rejection Control (ADRC) method by integrating Least Squares Estimation, establishing a direct relationship between the control law and the necessary frequency for the DSIM to turn in the exact speed whatever the load torque applied. The ADRC with Least Squares Estimation is compared with Field-Oriented Control (FOC) to evaluate their performance in tracking reference speed and stabilizing time. Additionally, another test is conducted to assess the ADRC's ability to reject disturbances. Results from the simulation demonstrate the effectiveness of ADRC with Least Squares Estimation, showcasing its potential for optimizing DSIM control in electric vehicle applications, thereby contributing to enhanced efficiency and stability.

Empowering industrial automation labs with IoT: A case study on real-time monitoring and control of induction motors using Siemens PLC and Node-RED

Empowering industrial automation labs with IoT: A case study on real-time monitoring and control of induction motors using Siemens PLC and Node-RED

Beta Maximum Power Extraction Operation-Based Model Predictive Current Control for Linear Induction Motors

There is an increasing interest in achieving global climate change mitigation targets that target environmental protection. Therefore, electric vehicles (as linear metros) were developed to avoid greenhouse gas emissions, which negatively impact the climate. Hence, this paper proposes a finite set-model predictive-based current control (FS-MPCC) strategy of linear induction motor (LIM) for linear metro drives fed by solar cells with a beta maximum power extraction (B-MPE) control approach to achieve lower thrust ripples and eliminate a selection of weighting factors, the main limitation of conventional model predictive-based thrust control (which can be time consuming and challenging). The B-MPE control approach ensures that the solar cells operate at their maximum power output, maximizing the energy harvested from the sun. Considering a single cost function of primary current errors between the predicted values and their references in αβ-axes, the proposed method eliminates the need for weighting factor selection, thus simplifying the control process. A comparison between the conventional and the presented control method is conducted using MATLAB/Simulink under different scenarios. Comprehensive simulation results of the presented system on a 3 kW LIM prototype revealed that the introduced system based on FS-MPCC surpasses the conventional technique, resulting in a maximum power extraction from solar cells and a suppression of the thrust ripples as well as an avoidance of weighting factor tuning, leading to fewer computational steps.