A Computationally Efficient Model Predictive Control of Six-Phase Induction Machines Based on Deadbeat Control
| Autor: | Antonio J. Marques Cardoso, João Serra, Imed Jlassi |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Control and Optimization Computer science Mechanical Engineering Computation Control (management) Phase (waves) model predictive current control deadbeat control execution time asymmetrical six-phase induction machine drive Industrial and Manufacturing Engineering Model predictive control Control and Systems Engineering Control theory Position (vector) TJ1-1570 Computer Science (miscellaneous) Mechanical engineering and machinery Electrical and Electronic Engineering computer Voltage reference computer.programming_language Voltage |
| Zdroj: | Machines, Vol 9, Iss 306, p 306 (2021) Machines; Volume 9; Issue 12; Pages: 306 |
| ISSN: | 2075-1702 |
| DOI: | 10.3390/machines9120306 |
| Popis: | Model predictive current control (MPCC) has recently become a viable alternative for multiphase electric drives, because it easily exploits the inherent advantages of multi-phase machines. However, the prediction in MPCC requires a high number of voltage vectors (VVs), being therefore computationally demanding. In that regard, this paper proposes a computationally efficient MPCC of an asymmetrical six-phase induction machine drive (ASIMD) that reduces the number of VVs used for prediction. By using the characteristics of the deadbeat control (DB), the proposed method obtains a reference voltage vector (RVV), where its position will serve as a reference and integrates the MPCC scheme. Only 4 out of 13 predictions are needed to determine the best VV, dramatically reducing the algorithm computation. Experimental results for a six-phase case study compare the standard MPCC with the suggested method, confirming that deadbeat model predictive current control (DB-MPCC) shows that the execution time can be shortened by 48.8% and successfully improve the motor performance and efficiency. |
| Databáze: | OpenAIRE |
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