scholarly journals Echtzeit-Implementierung von NMPC

atp magazin ◽  
2018 ◽  
Vol 60 (08) ◽  
pp. 46
Author(s):  
Stefan Löw ◽  
Dragan Obradovic
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Feedforward Neural Networks ◽  
Nonlinear Model Predictive Control ◽  
System Model ◽  
Mechatronic System ◽  
The Neural Networks ◽  
The Cost

Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

scholarly journals Echtzeit-Implementierung von NMPC

atp magazin ◽  
2018 ◽  
Vol 60 (08) ◽  
pp. 46-53 ◽  
Author(s):  
Stefan Löw ◽  
Dragan Obradovic
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Feedforward Neural Networks ◽  
Nonlinear Model Predictive Control ◽  
System Model ◽  
Mechatronic System ◽  
The Neural Networks ◽  
The Cost

Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

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