A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC

Luo, Yanhong; Zhao, Shengnan; Yang, Dongsheng; Zhang, Huaguang

doi:10.1109/jas.2019.1911798

Cited by 29 publications

(10 citation statements)

References 33 publications

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“…Cai et al 12 proposed a new ABC scheme to overcome the uncertainties for a class of second-order nonlinear systems with non-triangular uncertainties. At present, some control methods are combined with adaptive backstepping control methods to study classical nonlinear systems in the field of integer-order control method, such as adaptive backstepping sliding mode control (SMC), 13 adaptive neural network, or fuzzy backstepping, 14,15 adaptive dynamic surface backstepping control, 16 and so on.…”

Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Chen

Cao

Yuan

et al. 2021

Measurement and Control

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This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

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Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Chen

Cao

Yuan

et al. 2021

Measurement and Control

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“…A perceptron is a mathematical model, proposed by Frank Rosenblatt in 1957; it can be treated as a simple neural network used to classify the data into two classes. Neural networks are often trained using back propagation, however, evolutionary algorithms show promising results as well [39][40][41]. As shown in [26], a hybrid algorithm which incorporates evolutionary-inspired and classical optimization techniques can achieve faster convergence speed and better accuracy.…”

Section: Solution For the Linearly Inseparable Xor Problemmentioning

confidence: 99%

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Demidova

Gorchakov

2020

Algorithms

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Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network.

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“…Deep learning techniques have been widely used in various areas, such as biological neuronal system s [11], power systems [12] and autonomous vehicle systems [13]. Various works have been explored for deep learning approaches to recognise handwritten Arabic texts such as deep learning neural network, CNN, connectionist temporal classification (CTC), RNN, LSTM and bidirectional long short-term memory (BLSTM).…”

Section: Introductionmentioning

confidence: 99%

Quranic Optical Text Recognition Using Deep Learning Models

et al. 2021

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A Quranic optical character recognition (OCR) system based on convolutional neural network (CNN) followed by recurrent neural network (RNN) is introduced in this work. Six deep learning models are built to study the effect of different representations of the input and output, and the accuracy and performance of the models, and compare long short-term memory (LSTM) and gated recurrent unit (GRU). A new Quranic OCR dataset is developed based on the most famous printed version of the Holy Quran (Mushaf Al-Madinah), and a page and line-text image with the corresponding labels is prepared. This work's contribution is a Quranic OCR model capable of recognising the Quranic image's diacritic text. A better performance in word recognition rate (WRR) and character recognition rate (CRR) is achieved in the experiments. The LSTM and GRU are compared in the Arabic text recognition domain. In addition, a public database is built for research purposes in Arabic text recognition that contains the diacritics and the Uthmanic script, and is large enough to be used with the deep learning models. The outcome of this work shows that the proposed system obtains an accuracy of 98% on the validation data, and a WRR of 95% and a CRR of 99% in the test dataset.

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A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC

Cited by 29 publications

References 33 publications

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Quranic Optical Text Recognition Using Deep Learning Models

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