Performance analysis of novel robust ANN-MRAS observer applied to induction motor drive

Merrassi, Weam El; Abounada, Abdelouahed; Ramzi, Mohamed

doi:10.1007/s13198-021-01614-w

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…In recent several decades, NN-based techniques have gained great attention. The NNs are used as observers in [27], [28], adaptive NN is used to approximate unknown uncertainties in the system's dynamics [7], [21], [29], [30], and a variety of different types of nonlinear systems have been explored by using NNs-based adaptive backstepping techniques to deal with unknown nonlinearities [31]- [35].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Guo

Liu

2023

IEEE Access

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An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.

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Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Guo

Liu

2023

IEEE Access

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“…Therefore, the system requires a better performance measure to accurately detect emotional changes in humans. The human emotion recognition system is part of the artificial intelligence (AI) field [ 2 , 3 ], and this system includes the procedures of data processing, interpreting, and identifying emotional states [ 4 ]. The continuous development of AI technology, including deep learning and machine learning, is combined with an advanced clinical treatment which has helped to improve the classification of human emotion in recent years [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…erefore, the system requires a better performance measure to accurately detect emotional changes in humans. e human emotion recognition system is part of the arti cial intelligence (AI) eld [2,3], and this system includes the procedures of data processing, interpreting, and identifying emotional states [4].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network

Aung

Hassan²,

Brady

et al. 2022

Computational Intelligence and Neuroscience

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Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately reflect emotion in real-time. Although recent studies on this problem can provide acceptable performance measures, it is still not adequate for the implementation of a complete emotion recognition system. In this research work, we propose a new approach for an emotion recognition system, using multichannel EEG calculation with our developed entropy known as multivariate multiscale modified-distribution entropy (MM-mDistEn) which is combined with a model based on an artificial neural network (ANN) to attain a better outcome over existing methods. The proposed system has been tested with two different datasets and achieved better accuracy than existing methods. For the GAMEEMO dataset, we achieved an average accuracy ± standard deviation of 95.73% ± 0.67 for valence and 96.78% ± 0.25 for arousal. Moreover, the average accuracy percentage for the DEAP dataset reached 92.57% ± 1.51 in valence and 80.23% ± 1.83 in arousal.

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Performance analysis of novel robust ANN-MRAS observer applied to induction motor drive

Cited by 2 publications

References 35 publications

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network

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