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A Sphere Decoding Algorithm for Multistep Sequential Model-Predictive Control
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DOI：10.1109/TIA.2021.3060694.png)
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摘要
En 中文
This article investigates the combination of two model-predictive control concepts, i.e., sequential model-predictive control and long-horizon model-predictive control, for power electronics. To achieve sequential model-predictive control, the optimization problem is split into two subproblems. The first one summarizes all control goals, which linearly depend on the system inputs. Sequential model-predictive control generally requires to obtain more than one solution for the first subproblem. Due to the mixed-integer nature of finite control set model-predictive control power electronics, a special sphere decoder is, therefore, proposed in this article. The second subproblem consists of all those control goals that depend nonlinearly on the system inputs and is solved by an exhaustive search. The effectiveness of the proposed method is validated via numerical simulations at different scenarios on a three-level neutral-point-clamped permanent magnet synchronous generator wind turbine system and compared to other long-horizon model-predictive control methods.
Keyword:
Predictive control
Wind turbines
Predictive models
Decoding
Prediction algorithms
Photonic crystals
Magnetic materials
Finite control set
long horizon
model-predictive control (MPC)
sequential model-predictive control
sphere decoder
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期刊
IF:
4.5
论文数:
1.1W
被引数:
3.5W
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