Computationally Efficient Sphere Decoding Algorithm Based on Artificial Neural Networks for Long-Horizon FCS-MPC

Successful application of finite control set model predictive control strategies with long prediction horizon depends on the careful design of the optimization algorithm. The conventional method involves transforming the problem to an equivalent box-constrained integer least-squares formulation that...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 71; číslo 1; s. 39 - 48
Hlavní autoři: Zafra, Eduardo, Granado, Joaquin, Lecuyer, Vicente Baena, Vazquez, Sergio, Alcaide, Abraham M., Leon, Jose I., Franquelo, Leopoldo G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
Branch and bound methods
Capacitors
Control systems
DC–AC power conversion
Decoding
Design optimization
MIMO communication
neural network applications
Prediction algorithms
Predictive control
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ISSN:0278-0046, 1557-9948
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Shrnutí:Successful application of finite control set model predictive control strategies with long prediction horizon depends on the careful design of the optimization algorithm. The conventional method involves transforming the problem to an equivalent box-constrained integer least-squares formulation that can be solved with branch-and-bound techniques, such as the sphere decoding algorithm (SDA). In this work, it is proposed to define an artificial neural network (ANN) to replace the SDA, avoiding its inherent computational variability. Similarly to practical applications of the SDA, the ANN finds an approximate solution of the underlying optimization problem. In contrast, the main benefit of the proposed approach is that it can be implemented in a low-cost microprocessing platform, greatly improving the performance in terms of resources in comparison with other advanced techniques proposed in the literature.
Bibliografie:ObjectType-Article-1
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2023.3243301