Extended State Observer Based Interval Type-2 Fuzzy Neural Network Sliding Mode Control With Its Application in Active Power Filter

Liu, Lunhaojie; Fei, Juntao

doi:10.1109/tpel.2021.3127896

Cited by 19 publications

(6 citation statements)

References 34 publications

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“…As shown in Figure 1 , at present, modern evaluation of quality of English teaching theory appears, and intelligent analysis has been introduced into evaluation of quality of English teaching work in universities and colleges. The traditional analysis method is based on the previous experience of evaluation of the quality of English teaching method, while the intelligent method is based on the modern intelligent calculation theory of evaluation of the quality of English teaching method [ 10 ].…”

Section: Basic Concepts Of Fuzzy Nn and The Study Of English Evaluati...mentioning

confidence: 99%

A Study of College Teachers’ English Teaching Quality Based on Fuzzy Neural Network

Chen

2022

Computational Intelligence and Neuroscience

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In many universities and colleges, the government is now paying more attention to the quality of teaching assessment, and research on English teaching quality evaluation is becoming increasingly significant. The goal of this paper is to investigate how to use a fuzzy neural network to assess the quality of English education. The research of the model NN and the evaluation of the quality of English teaching is detailed first. A fuzzy NN is an algorithm that examines the quality of English instruction evaluation using fuzzy criteria. In the case results, the data show that the previous evaluation of quality teaching is between the intermediate and the intermediate level, while the research on the evaluation quality of English teaching based on fuzzy NN shows that the evaluation quality is between the intermediate and the advanced level. Therefore, the combination of Fuzzy Rules (FR) and NN methods can effectively improve the quality of college teachers’ English teaching evaluation. In college English teaching, FRs can simplify the analysis steps of evaluation of quality teaching, accurately judge the quality of college English teaching evaluation, and provide support for the improvement of evaluation of quality teaching.

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Section: Basic Concepts Of Fuzzy Nn and The Study Of English Evaluati...mentioning

confidence: 99%

A Study of College Teachers’ English Teaching Quality Based on Fuzzy Neural Network

Chen

2022

Computational Intelligence and Neuroscience

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“…The second contribution of this paper is the development of a state-feedback controller, designed to enhance the performance of the proposed hybrid filter, especially in systems with complex impedance conditions, such as those in industrial environments with harmonic resonances. The control strategies for hybrid filters traditionally use wideband approaches, with proportional control being the most common approach [26][27][28][29][30][31]. However, the high proportional constants needed for effective filtering tend to compromise transient performance with the proposed topology [25].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the poor dynamic behavior of this topology under fluctuating loads has been documented in [32], where a proportional-integral (PI) regulator implemented in the harmonic reference frame (HRF) was used to control the active part of the hybrid filter. Other authors have analyzed various control techniques such as sliding-mode control [28] and fuzzy neural network control [29], which enable the precise tracking of reference signals. However, these techniques increase the system's order and complexity, making the application of such advanced control methods more challenging.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Evaluation of a Hybrid Active Power Filter Controller

Herman,

Knez,

Blažič

2024

Energies

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This paper introduces a novel hybrid filter topology that combines passive and active components to enhance harmonic filtering and resonance damping in electrical power systems. The design integrates a three-phase two-level voltage-source converter with a double-tuned passive filter in parallel, significantly reducing the power rating and operational costs while maintaining good harmonic filtering performance and reactive current compensation. Double-tuned passive filters, compared to single-tuned ones, offer improved harmonic attenuation at multiple frequencies, enhancing overall system efficiency. Moreover, when used with the proposed hybrid filter topology, the double-tuned version allows for even lower dimensions of the active part, thereby further reducing system cost. A state-feedback controller is designed to enhance the performance of the hybrid filter, proving particularly effective in environments with complex impedance conditions. This paper also examines the impact of variations in passive component parameters, demonstrating the design’s robustness against potential deviations expected over the operational lifespan. The results indicate that the hybrid filter effectively mitigates harmonics and maintains operational stability under various transient conditions, as confirmed by analytical and simulation studies on a real industrial network model. These findings underline the hybrid filter’s potential to significantly improve power quality in modern electrical networks.

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“…Li et al utilized the RBFNNs to estimate the unknown function in nonlinear systems [26]. The combination of fuzzy logic rules and neural networks can reduce the number of nodes of neural networks and improve the ability to deal with nonlinearity and robustness [27][28][29][30]. However, due to the fact that the Type-1 fuzzy method uses accurate and clear membership functions, the overall performance of the system could not reduce or eliminate the uncertainties effectively caused by changes in the environment and other factors.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks

2023

Self Cite

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Aiming at the unknown uncertainty of an active power filter system in practical operation, combining the advantages of self-feedback structure, interval type-2 fuzzy neural network, and super-twisting sliding mode, an adaptive super-twisting sliding mode control method of interval type-2 fuzzy neural network with self-feedback recursive structure (IT2FNN-SFR STSMC) is proposed in this paper. IT2FNN has an uncertain membership function, which can enhance the nonlinear ability and robustness of the network. The historical information will be stored and utilized by the self-feedback recursive structure (SFR) at runtime. Therefore, the novel IT2FNN-SFR is designed to improve the dynamic approximation effect of the neural network and reduce the dependence of the controller on the actual mathematical model. The adaptive rate of each weight of the neural network is designed by the Lyapunov method and gradient descent (GD) algorithm to ensure the convergence and stability of the system. Super-twisting sliding mode control (STSMC) has strong robustness, which can effectively reduce system chattering, and improve control accuracy and system performance. The gain of the integral term in the STSMC is set as a constant, and the other gain is changed adaptively whose adaptive rate is deduced through the stability proof of the neural network, which greatly reduces the difficulty of parameter adjustment. The harmonic suppression ability of the designed control strategy is verified by simulation experiments.

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Extended State Observer Based Interval Type-2 Fuzzy Neural Network Sliding Mode Control With Its Application in Active Power Filter

Cited by 19 publications

References 34 publications

A Study of College Teachers’ English Teaching Quality Based on Fuzzy Neural Network

A Study of College Teachers’ English Teaching Quality Based on Fuzzy Neural Network

Design and Performance Evaluation of a Hybrid Active Power Filter Controller

Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks

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