Design a New Intelligent Control for a Class of Nonlinear Systems

Tavoosi, Jafar; Mohammadi, Fazel

doi:10.1109/iccia49288.2019.9030868

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…In [17], an adaptive neural network (NN)-based observer was implemented to nonlinear systems subjected to disturbances. The meaning of "adaptive" in [16,17] can be identical to the meaning of "adaptive" in this paper. However, there is a difference between them.…”

Section: Hypothesis Test For Validity Checkmentioning

confidence: 70%

“…In adaptive control of nonlinear systems, the acquisition of a number of additional data online may be feasible. For example, Mohammadi et al [16] used a radial basis function neural networks (RBFNN)-based controller to approximate the functions of uncertain nonlinear systems. The focus was on how to deal with uncertainty in model parameters.…”

Section: Hypothesis Test For Validity Checkmentioning

confidence: 99%

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A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

et al. 2020

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For model verification and validation (V & V) in computational mechanics, a hypothesis test for the validity check (HTVC) is useful, in particular, with a limited number of experimental data. However, HTVC does not address how type I and II errors can be reduced when additional resources for sampling become available. For the validation of computational models of safety-related and mission-critical systems, it is challenging to design experiments so that type II error is reduced while maintaining type I error at an acceptable level. To address the challenge, this paper proposes a new method to design validation experiments, response-adaptive experiment design (RAED). The RAED method adaptively selects the next experimental condition from among candidates of various operating conditions (experimental settings). RAED consists of six key steps: (1) define experimental conditions, (2) obtain experimental data, (3) calculate u-values, (4) compute the area metric, (5) select the next experimental condition, and (6) obtain additional experimental datum. To demonstrate the effectiveness of the RAED method, a case study of a numerical example is shown. It is demonstrated that additional experimental data obtained through the RAED method can reduce type II error in hypothesis testing and increase the probability of rejecting an invalid computational model.

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Section: Hypothesis Test For Validity Checkmentioning

confidence: 70%

Section: Hypothesis Test For Validity Checkmentioning

confidence: 99%

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

et al. 2020

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“…Design of robust higher order sliding mode control strategy for hybrid microgrids were discussed by Cucuzzella et al and Baghaee et al [13,14]. Mohammadi et al also reported some works on power management strategy in multi-terminal VSC-HVDC system, adaptive neural observer-based nonsingular terminal sliding mode controller & intelligent controller design for a class of nonlinear systems, bidirectional power charging control strategy for plug-in hybrid EVs for MT-HVDC grids [15][16][17][18][19][20][21][22]. In proposed work, the constant switching SMC is described in which the switching losses are reduced as compared to the primitive SMC by maintaining a constant switching frequency.…”

Section: Introductionmentioning

confidence: 99%

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

Panda

Ghosh

2020

Technol Econ Smart Grids Sustain Energy

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Power electronics converter and inverter systems for microgrid operations are always stochastic. When the system is operating in grid-tied mode, the attached load is not constant, so the current which drawn from the DC grid also varies leads to a voltage drop and that causes steady-state error in between the actual and desired output voltage in microgrid. DC-DC bidirectional converters are widely used in microgrid application that facilitates bidirectional energy convertion which having boost mode operation in one direction and bucking mode in other operation. The Interfacing converter is controlled by using the Maximum Power Point Tracking (MPPT) control technique for obtaining maximum power during different atmospheric conditions. In this work, a Model Predictive Control (MPC) strategy is proposed which having an improved cost function, single prediction horizon, and an observer to keep tracking the output voltage. Here, MPC is proposed for cumulative control in between the DC grid side converters and AC grid side inverter, and for improving the power quality and reliability of the utility grid. Both continuous time and discrete time domain models are formed for the whole system for applying different control strategies. Along with MPC, the system has been controlled by using other controllers like as PI controller, Sliding Mode Controller (SMC). Luenberger observer is used to check the DC grid voltage when disturbances are implemented. Here, all the computational results are compared for justifying and validate a better controlling method for microgrid application. These kind of work is first time reported in this manuscript.

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“…2020, 10, 1329 2 of 18 their shortcomings in learning ability, processing efficiency, and feature extraction ability. For example, the learning ability of the fuzzy methods are not satisfying [7,8]. Neural networks (NN) tend to fall into local optimal solutions [9,10].…”

mentioning

confidence: 99%

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

Duan

2020

Applied Sciences

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With the development of Industry 4.0, as a pivotal part of the power system, large-capacity power transformers are requiring fault diagnostic methods with higher intelligence, accuracy and anti-interference ability. Considering the powerful capability for extracting non-linear features and the sensitivity differences to features of deep learning methods, this paper proposes a deep parallel diagnostic method for transformer dissolved gas analysis (DGA). In view of the insufficient and imbalanced dataset of transformers, adaptive synthetic oversampling (ADASYN) was implemented to augment the fault dataset. Then, the newly constructed dataset was normalized and input into the LSTM-based diagnostic framework. Then, the dataset was converted into images as the input of the CNN-based diagnostic framework. At the same time, the problem of still insufficient data was compensated by the introduction of transfer learning technology. Finally, the diagnostic models were trained and tested respectively, and the Dempster-Shafer (DS) evidence theory was introduced to fuse the diagnostic confidence matrices of the two models to achieve deep parallel diagnosis. The results of the proposed deep parallel diagnostic method show that without complex feature extraction, the diagnostic accuracy rate could reach 96.9%. Even when the dataset was superimposed with 3% random noises, the rate only decreased by 0.62%.Appl. Sci. 2020, 10, 1329 2 of 18 their shortcomings in learning ability, processing efficiency, and feature extraction ability. For example, the learning ability of the fuzzy methods are not satisfying [7,8]. Neural networks (NN) tend to fall into local optimal solutions [9,10]. The K-nearest neighbor (KNN) method is inefficient in high-dimensional space [11]. The support vector machine (SVM) is essentially a two-classifier, which makes it more troublesome to deal with multi-classification problems [12,13]. There are also some relevant diagnosis studies on combining traditional ratio methods and intelligent methods, which are combinations of previous researches, and have achieved certain effects [14][15][16]. However, the features extracted by the ratio methods are still limited. And the anti-interference ability of the combined models need further testing. In recent years, due to the strong ability of deep learning to extract complex non-linear features, some papers have tried to introduce it into the field of transformer fault diagnosis. And the fault diagnostic accuracy of deep learning methods has been significantly improved compared to traditional machine learning diagnostic models'. The authors in [17] proposed a DBN-based fault diagnostic method that used the uncoded ratio of DGA data as the model input. Compared with the traditional methods', the accuracy was significantly improved. The authors in [18] introduced a method for identifying and locating winding faults based on CNN and transformer impulse tests. And the results showed that the method was effective. The authors in [19] studied the internal fault diagnosis of...

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Design a New Intelligent Control for a Class of Nonlinear Systems

Cited by 6 publications

References 20 publications

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

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