Intelligent Terminal Sliding Mode Control of Active Power Filters by Self-Evolving Emotional Neural Network

Chu, Yundi; Fu, Shili; Fei, Juntao

doi:10.1109/tii.2022.3168654

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…The supervised gradient descent method is applied to update the weights and the related parameters of the RWFNN controller. For the controls in the regulated fundamental positive-sequence extraction (section A) and DC-link voltage regulation (section B), the objective function O(N) can be defined in Equations ( 14) and (15), respectively. 14)…”

Section: Learning Process Of Rwfnn-based Controllermentioning

confidence: 99%

“…In general, it is difficult to obtain the specific values of the parameters for the all-neural network-based controllers with the mathematical derivation. The parameters for the RNN, FNN, and RWFNN-based controllers are determined with the experimentation, where the initial values of the parameters are set small and updated to the suitable ones with satisfactory control response [15]. The parameters of the test system are displayed in Table 1.…”

Section: Case Studiesmentioning

confidence: 99%

“…In [14], the feature selection method with a recurrent neural network is proposed to fit the uncertain function and improve the performance of the conventional global sliding mode control for the SAPF. A self-evolving emotional neural network is applied in [15] to implement a model-free control system to provide fast convergence and global robustness of the SAPF controller. From the above-mentioned researches, it is found that the proposals are mainly focused on the improvement of the SAPF compensation accuracy to meet the requirement of the harmonic injection to the power system in IEEE Standard 519-2022 [16].…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter

Chen

2022

Energies

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The usage of a shunt active power filter (SAPF) is one of the helpful means to mitigate the reactive power and harmonic current of a power grid. The compensation performance of the SAPF is related to the accuracy of the reference voltage extraction from the utility grid, the control stability of the DC-link voltage regulation, and the synchronization between the source voltage and the reference compensation current. To modify the performance of the SAPF for the harmonic compensation, the control strategy of the SAPF reference compensation current based on the recurrent wavelet fuzzy neural network (RWFNN) is proposed in this paper. There are three sections in the proposed control strategy, including the regulated fundamental positive-sequence extraction (section A), DC-link voltage regulation (section B), and calculation of reference compensation current (section C). By regulating the analysis mechanism with the variation of fundamental frequency in the section A, the accurate reference voltage would be obtained. The control stability for the regulation of the DC-link voltage can be accomplished by applying the RWFNN-based controller in the section B. With the synchronized reference voltage in the section A and the estimated control current in the section B, the reference compensation current can be correctly obtained in the section C. From the case studies with the real-time simulator produced by OPAL-RT Technologies Inc., the effectiveness of proposed control strategy for the SAPF reference compensation current can be verified.

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Section: Learning Process Of Rwfnn-based Controllermentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter

Chen

2022

Energies

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“…Many researchers have made deep studies for RFNN and acquired lots of advanced achievements in recent years 25–34 . Advanced neural network strategy with sliding mode schemes has been researched for various dynamic systems 35–41 . For various dynamic systems, advanced intelligent controllers containing neural strategy and fuzzy technique have been employed to design effective approximation schemes 42,43 …”

Section: Introductionmentioning

confidence: 99%

“…[25][26][27][28][29][30][31][32][33][34] Advanced neural network strategy with sliding mode schemes has been researched for various dynamic systems. [35][36][37][38][39][40][41] For various dynamic systems, advanced intelligent controllers containing neural strategy and fuzzy technique have been employed to design effective approximation schemes. 42,43 Considering the different connection way of feedback signals, existing RFNN mainly contains the following two structures: internal loop feedback and external loop feedback.…”

Section: Introductionmentioning

confidence: 99%

Neuroadaptive global sliding mode control for nonlinear systems and its application

Chu

Fei

Hou

2023

Intl J Robust & Nonlinear

Self Cite

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One type of adaptive controller on the basis of an original dual-loop recursive fuzzy neural network (DRFNN) with two closed loop structures for a kind of nonlinear dynamic systems is designed in this paper. Though the conventional FNN could approximate arbitrary smooth functions, the inner network parameters need to be set up in advance. Usually, the setting of the base width and central vector are lack of theoretical guidance to some degree and need to be debugged many times. However, the initial values of the center vector and the base width can be arbitrarily set and stable to optimal ones in the light of adaptive mechanism of the proposed dual recursive FNN. Besides, the dynamic dual recursive FNN possesses the ability of storing more beneficial information through constructing signal back loops, meanwhile accuracy of the approximation is higher than the traditional FNN. Furthermore, the learning speed and detection accuracy can be improved due to the fact that expert knowledge is a priori knowledge in the network structure by including if-then rules of this FNN. To validate effectiveness of the developed scheme, simulations and three suits of experiments are carried out on a set of active power filter to demonstrate that this presented global sliding mode control using DRFNN can achieve expected performance. Eventually, some comparisons among FNN and the proposed DRFNN are implemented to suggest the dual recursive FNN can obtain more superior properties.

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Robust hybrid intelligent control using probabilistic feature for active power filter

Chu,

Luo,

Hou

et al. 2023

Control Engineering Practice

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Intelligent Terminal Sliding Mode Control of Active Power Filters by Self-Evolving Emotional Neural Network

Cited by 9 publications

References 43 publications

Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter

Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter

Neuroadaptive global sliding mode control for nonlinear systems and its application

Robust hybrid intelligent control using probabilistic feature for active power filter

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