Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

Khammar, F.; Debbache, Nasr Eddine

doi:10.1155/2016/8052027

Cited by 10 publications

(3 citation statements)

References 6 publications

(16 reference statements)

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“…This includes feedback linearization, which transforms nonlinear dynamics into a linear form and facilitates the application of classical control techniques. Passivity control, sliding mode control, artificial intelligence control, Lyapunov control and adaptive control are other approaches to improve stability and control accuracy through understanding the nonlinearity of system dynamics [35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Symmetric Nonlinear Feedback Control and Machine Learning for Sustainable Spherical Motor Operation

Hassan,

Beshr,

Beshr

et al. 2023

Symmetry

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This paper presents a comprehensive evaluation of a new control technique for the sphere motor system, aimed at achieving accurate tracking, robust and dispersion of vibrations. Control methods include the determination of a nonlinear model and the application of feedback linearization, followed by the optimization of the proportional derivative (PD) coefficients through the Adaptive Neuro-Fuzzy Inference System. In addition, the system’s reaction to harsh environments is managed using Long Short-Term Memory. In order to gain a deeper understanding, symmetrical environmental disturbances and trajectories are introduced during the testing phase. The results demonstrate the superior performance of the control strategy, with reduced vibrations, faster recovery and confirmed tracking accuracy. In addition, the control method shows its adaptability and reliability, as evidenced by the significant reduction in CO2 emissions compared to conventional PD control methods. The use of symmetric trajectories and visualizations further emphasizes the behavior of the system under symmetric conditions, strengthening the effectiveness and applicability of the control strategy in real-world scenarios. Overall, this study presents a promising solution for converting complex systems under different conditions and making them potentially applicable in various industrial contexts.

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Section: Introductionmentioning

confidence: 99%

Symmetric Nonlinear Feedback Control and Machine Learning for Sustainable Spherical Motor Operation

Hassan,

Beshr,

Beshr

et al. 2023

Symmetry

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“…AM is a nonlinear machine, where the influence of temperature, age, and additional vibration elements related to electromagnetism affect the operation of the machine. So, with this idea, the authors in [34], after mathematically modeling AM, defined a control strategy based on rotor flux, which ensures the robustness of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

et al. 2022

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A synchronous machine is an electro-mechanical converter consisting of a stator and a rotor. The stator is the stationary part of a synchronous machine that is made of phase-shifted armature windings in which voltage is generated and the rotor is the rotating part made using permanent magnets or electromagnets. The excitation current is a significant parameter of the synchronous machine, and it is of immense importance to continuously monitor possible value changes to ensure the smooth and high-quality operation of the synchronous machine itself. The purpose of this paper is to estimate the excitation current on a publicly available dataset, using the following input parameters: Iy: load current; PF: power factor; e: power factor error; and df: changing of excitation current of synchronous machine, using artificial intelligence algorithms. The algorithms used in this research were: k-nearest neighbors, linear, random forest, ridge, stochastic gradient descent, support vector regressor, multi-layer perceptron, and extreme gradient boost regressor, where the worst result was elasticnet, with R2 = −0.0001, MSE = 0.0297, and MAPE = 0.1442; the best results were provided by extreme boosting regressor, with R2¯ = 0.9963, MSE¯ = 0.0001, and MAPE¯ = 0.0057, respectively.

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“…e satisfactory load current ripple performance can be yielded; however, it is computationally intensive and the average switching frequency is still lower than 1/T s . However, with these above MPC-RCMV strategies, the current and torque ripples may be larger than the conventional field-oriented control (FOC) [23,24] with the same control frequency (1/T s ), mainly owing to that the average switching frequency is far smaller than 1/T s . In addition, the switching frequency of these methods are not fixed, which make it hard to analyze the harmonic features and design the filter.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Method with Constant Switching Frequency to Reduce Common-Mode Voltage for PMSM Drives

Chen

et al. 2018

Journal of Electrical and Computer Engineering

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A model predictive control method to reduce the common-mode voltage (MPC-RCMV) with constant switching frequency for PMSM drives is proposed in this paper. Four nonzero VVs are adopted in future control period and the switching sequence is designed to ensure the switching frequency is fixed and equal to the control frequency. By substituting the finite-control nonzero voltage vectors in the current predictive model, a current predictive error space vector diagram is obtained to determine the adopted four VVs. The duty ratio calculating method for the selected four VVs is studied. Compared with the conventional MPC-RCMV method, the current and torque ripples are greatly reduced and the switching frequency is fixed. The simulation and experiment results validate the effectiveness of the proposed method.

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Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

Cited by 10 publications

References 6 publications

Symmetric Nonlinear Feedback Control and Machine Learning for Sustainable Spherical Motor Operation

Symmetric Nonlinear Feedback Control and Machine Learning for Sustainable Spherical Motor Operation

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

Model Predictive Control Method with Constant Switching Frequency to Reduce Common-Mode Voltage for PMSM Drives

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