Neural network‐based command filtered control for induction motors with input saturation

Fu, Cheng; Zhao, Lin; Yu, Jinpeng; Yu, Haisheng; Lin, Chong

doi:10.1049/iet-cta.2017.0059

Cited by 23 publications

(25 citation statements)

References 35 publications

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“…It is a complex nonlinear relationship between rugosity M ij,1 , standard deviation M ij,2 , skewness M ij,3 , kurtosis M ij, 4 and feed speed Vc, peripheral speed Vw, axial displacement f a , radial displacement f r in (18). The nonlinear relationship between them is described using deep neural networks.…”

Section: Grinding Robot Prediction Algorithm Of Controllermentioning

confidence: 99%

“…Adaptive neural network controllers were studied in many aspects. The approximation-based controllers are designed for induction motors with input saturation [4] and 3-DOF robotic manipulator that is subject to backlash like hysteresis and friction [5]. The parameter-based controllers are designed to identify the unknown robot kinematic and dynamic parameters for robot manipulators with finite-time convergence [6] and perform haptic identification for uncertain robot manipulators [7].…”

Section: Introductionmentioning

confidence: 99%

“…(2) In order to verify the eigen solution theory of neural networks, the morphological subfeature graph with multimoment was constructed to describe the block surface morphology using rugosity M ij,1 , standard deviation M ij,2 , skewness M ij,3 , and kurtosis M ij, 4 and then build prediction model with deep learning neural networks for controller parameters of the grinding robot. The predicted accuracies of the proposed model are more than 95%.…”

Section: Introductionmentioning

confidence: 99%

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Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Tang

et al. 2019

Complexity

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The robot dynamic model is often rarely known due to various uncertainties such as parametric uncertainties or modeling errors existing in complex environments. It is a key problem to find the relationship between the changes of neural network structure and the changes of input and output environments and their mutual influences. Firstly, this paper defined the conceptions of neural network solution, neural network eigen solution, neural network complete solution, and neural network partial solution and the conceptions of input environments, output environments, and macrostructure of neural networks. Secondly, an eigen solution theory of general neural networks was proposed and proven including consistent approximation theorem, eigen solution existence theorem, consistency theorem of complete solution, the partial solution, and none solution theorem of neural networks. Lastly, to verify the eigen solution theory of neural networks, the proposed theory was applied to a novel prediction and analysis model of controller parameters of grinding robot in complex environments with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. The morphological subfeature graph with multimoment was constructed to describe the block surface morphology using rugosity, standard deviation, skewness, and kurtosis. The results of theoretical analysis and experimental test show that the output traits have an optional effect with joint action. When the input features functioning in prediction increase, higher predicted accuracy can be obtained. And when the output traits involving in prediction increase, more output traits can be predicted. The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. Compared with the traditional prediction model, the proposed model can predict output features simultaneously and is more stable.

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Section: Grinding Robot Prediction Algorithm Of Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Tang

et al. 2019

Complexity

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“…In [21], a feedback control method combining the backstepping control and fuzzy controllers was adopted, which could approximate nonlinear functions and achieve good tracking. In [22], an artificial neural network was added to the backstepping control to control the induction motor drive system, which could efficiently estimate uncertain parameters online.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Command-Filtered Backstepping Control for Virtual Synchronous Generators

Cui

Yang

et al. 2019

Energies

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Distributed energy sources are usually interfaced to the grid using power electronic converters, and lack of inertia in inverter dominated microgrids can affect the system stability. This paper presents a new method for virtual synchronous generator (VSG) control in order to solve the low system inertia and support the grid frequency problem. In this paper, the VSG based on electromagnetic transient characteristics is improved and an adaptive command filter back-stepping controller is designed. Firstly, the rotor swing equation and power part are modeled to complete the controller design for achieving system stability in the islanded, grid-connected and transition modes. In addition, a limited-amplitude command filter is used to deal with computational complexity and nonlinear saturation problems in the design process. Secondly, projection operator, and adaptive inertia and damping control are introduced to reduce the modeling error and disturbance caused by changing parameters. This ensures the boundedness of the estimated value and further improves the frequency response, especially in the transition mode. Finally, simulation results show that the proposed controller is more effective than the traditional control method for achieving power stability and frequency improvement.

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“…Some scientists try to predict the accuracy of components produced by Wire Electrical Discharge Machining (WEMD) by using an Elman-based Layer Recurrent Neural Network (LRNN) [1]. Several studies discuss a novel conceptual framework: Fractional Hopfield neural networks (FHNN), neural network-based command filtered control for induction motors with input saturation, design of a neural network based distributed power flow controller (DPFC) for power system stability, while others observe the feedback principle [2,3,4,5]. Deisseroth and Schnitzer study engineering approaches to illuminating brain structure and dynamics.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks as the data ruling components of future intelligent energy systems

Buran

Denisov

2017

MATEC Web Conf.

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Abstract. The main target of this work is to study artificial neural networks and their role in the future intelligent energy systems. Artificial neural networks in the near future will be a very effective tool for data analysis and very perspective for energy consumers. The accelerating growth in the field of software engineering research will prove appearing of neural networks in intelligent energy systems. This study is expected to be important for researchers and engineers studying artificial neural networks (ANNs) in a wide range of domains.

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Neural network‐based command filtered control for induction motors with input saturation

Cited by 23 publications

References 35 publications

Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Adaptive Command-Filtered Backstepping Control for Virtual Synchronous Generators

Artificial neural networks as the data ruling components of future intelligent energy systems

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