scholarly journals Output Control Method of Microgrid VSI Control Network Based on Dynamic Matrix Control Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 158459-158480
Author(s):  
Runnan Dong ◽  
Shi Liu ◽  
Geng Liang ◽  
Xin An ◽  
Yuanyuan Xu
Author(s):  
Ali Thamallah ◽  
Anis Sakly ◽  
Faouzi M’Sahli

This article focuses on the tracking and stabilizing issues of a class of discrete switched systems. These systems are characterized by unknown switching sequences, a non-minimum phase, and time-varying or dead modes. In particular, for those governed by an indeterminate switching signal, it is very complicated to synthesize a control law able to systematically approach general reference-tracking difficulties. Taking into account the difficulty to express the dynamic of this class of systems, the present paper presents a new Dynamic matrix control method based on the multi-objective optimization and the truncated impulse response model. The formulation of the optimization problem aims to approach the general step-tracking issues under persistent and indeterminate mode changes and to overcome the stability problem along with retaining as many desirable features of the standard dynamic matrix control (DMC) method as possible. In addition, the formulated optimization problem integrates estimator variables able to manipulate the optimization procedure in favor of the active mode with an appropriate adjustment. It also provides a progressive and smooth multi-objective control law even in the presence of problems whether in subsystems or switching sequences. Finally, simulation examples and comparison tests are conducted to illustrate the potentiality and effectiveness of the developed method.


2010 ◽  
Vol 163-167 ◽  
pp. 2666-2669
Author(s):  
Zhi Xiang Yin ◽  
Zhe Gao ◽  
Yao Feng

It has been proved that Multi-layer network computing can solve the nonlinear separable problem, but the problem which the hidden layer makes the learning more difficult limits the development of multi-layer network.Back-propagation (BP) algorithm solve this problem and promote multi-network research to regain attention. In this paper, A new method which dynamic matrix control method based on neural network is found. Its essence is that the resulting prediction signal is produced by the manner which regards neural network model as prediction model, and the predictive control of nonlinear systems would be realized by the control law which using the receding optimization algorithm. Neural network Selects the BP neural network which possess a good nonlinear function approximation capability.Aiming at dongping tunnel surface deformation prediction, the article adopt BP Neural Network to train the system basing on the given data. It shows the hiding neural node is close to precision; predictive value in good agreement with measured values, and to some extent be able to guide the construction.


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