Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Affan, Muhammad; Uddin, Riaz

doi:10.1007/s12555-020-0306-z

Cited by 10 publications

(4 citation statements)

References 30 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FOC strategy is popular for 3-phase IM drives thanks to its high accuracy [31][32][33]. The FOC method based on the rotor flux keeps the magnitude of the rotor flux constant, while the rotor flux angular position varies.…”

Section: Traditional Dfoc Ekfmentioning

confidence: 99%

A novel control method for improving reliability of 3‐phase induction motor drives under the stator winding open‐circuit fault

Ghojoghnezhad,

Ghanbari,

Ebrahimi

et al. 2023

IET Electric Power Appl

View full text Add to dashboard Cite

Post‐fault control of a 3‐phase Induction Motor (IM) drive under the Stator winding Open‐Circuit Fault (SOCF) has gained significant importance in recent years especially for safety‐critical industrial applications. Nevertheless, most of proposed schemes suffer from low accuracy or high complexity. In this research, a novel control scheme for improving reliability of wye‐connected 3‐phase IM drives under the SOCF according to Direct Field‐Oriented Control (DFOC) is proposed and evaluated. In the proposed system, different transformations are presented and employed to control the faulty system. The controller is achieved by minor modifications in the traditional control scheme structure. In the proposed system, an Extended Kalman Filter (EKF) is also used to estimate the rotor flux. The designed EKF‐based DFOC (DFOCEKF) is verified using simulations and experimental tests conducted on a 0.75 kW 3‐phase IM drive. Based on the results it can be calculated that the torque ripple is decreased clearly after the proposed fault‐tolerant control approach is applied. The DFOCEKF strategy can control the faulty motor accurately, which can further improve the reliability of IM drive systems. The results also confirmed that the proposed controller after the SOCF has faster dynamic response and higher accuracy compared to previous published papers.

show abstract

Section: Traditional Dfoc Ekfmentioning

confidence: 99%

A novel control method for improving reliability of 3‐phase induction motor drives under the stator winding open‐circuit fault

Ghojoghnezhad,

Ghanbari,

Ebrahimi

et al. 2023

IET Electric Power Appl

View full text Add to dashboard Cite

show abstract

“…Equations ( 1) -(3) describe the switching characteristics for the three-phase bridge VSI. One should notice that the transistor does not necessarily turn on when it is commanded to conduct; the transistor operates only if the on-switching command is provided, and the current direction corresponds to the characteristics of the transistor [17][18][19][20] .…”

Section: 1switched Model Of Two-level Vsimentioning

confidence: 99%

Slip-Ring Induction Motor Torque and Current Ripple Minimization Using Bang-Bang Current Control

Emar¹,

Al-Omari²,

Rawashdeh³

2023

Evergreen

View full text Add to dashboard Cite

This paper presents a significant improvement in the dynamic performance and control of an isolated slip-ring induction motor (SRIM). The stator windings of this SRIM-based wound rotor motor are connected to a fixed dc voltage PV system through a boost converter (BC) and a twolevel three-phase voltage source inverter (VSI). Thus, SRIM is modulated and controlled using a nonlinear ramp bang-bang current-mode technique (RBBCM) with PWM a modulated inverter VSI. Using Simplorer 7, and MATLAB, the experimental and simulation results are investigated and compared between these techniques.The results show the following: the improvement, which can be seen by modulating the fixed frequency and waveform achieved by the inverter; The phase shift between the main component of the load phase voltage and current is almost zero; In addition, the variable hysteresis modulation successfully cancels noise and disturbances while also mitigating both higher and lower-order harmonics. The experimental results are investigated and compared with respect to total harmonic distortion, transient response of SRIM speed and developed torque, and other factors that may affect the SRIM performance.

show abstract

“…The BEL approach is gaining traction across various applications, including earthquake prediction, where emotional impact plays a significant role in understanding and preparing for seismic events [21]. Novel control strategies that integrate Brain Emotional Learning with Adaptive Model Predictive Control [22] have been developed for induction motor drives, while methods combining Recursive Terminal Sliding-Mode Control (RTSMC) with Double Hidden Layer Fuzzy Emotional Recurrent Neural Network (DHL-FERNN) have improved robustness and adaptability in controlling nonlinear systems [23]. However, emotional learning-based controllers, including the nonparametric ELBC, encounter challenges such as computational complexity and lack of robustness, especially in uncertain environments [24].…”

Section: Introductionmentioning

confidence: 99%

Bias-Boosted ELM for Knowledge Transfer in Brain Emotional Learning for Time Series Forecasting

Iamsa-At,

Horata,

Sunat

2024

IEEE Access

View full text Add to dashboard Cite

This paper presents the Bias-Boosted Extreme Learning Machine guided Brain Emotional Learning (B2ELM-BEL) model, a significant advancement in chaotic time series prediction that effectively incorporates knowledge transfer learning. Integrating traditional Brain Emotional Learning (BEL) with the novel Biased-ELM method, the B2ELM-BEL introduces a bias term into the output weights of Extreme Learning Machines (ELM). This addition enhances the model's predictive accuracy, proving particularly beneficial in configurations with a minimal number of hidden nodes. Our evaluation of the B2ELM-BEL model across various datasets, including complex chaotic time-series benchmarks and real-world scenarios, demonstrates its superior performance over several BEL models. It achieves lower mean RMSE, MAE, and SMAPE values, and exhibits enhanced generalizability and efficiency. The findings indicate that while the single hidden node variant of B2ELM-BEL is apt for simpler tasks, the multi-node version is more adept at handling challenging environments. This highlights the necessity of tailoring the model to the complexity of the specific dataset being analyzed.

show abstract

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Cited by 10 publications

References 30 publications

A novel control method for improving reliability of 3‐phase induction motor drives under the stator winding open‐circuit fault

A novel control method for improving reliability of 3‐phase induction motor drives under the stator winding open‐circuit fault

Slip-Ring Induction Motor Torque and Current Ripple Minimization Using Bang-Bang Current Control

Bias-Boosted ELM for Knowledge Transfer in Brain Emotional Learning for Time Series Forecasting

Contact Info

Product

Resources

About