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Hybrid Neural Current Controllers for Decentralized and Highly Nonsinusoidal Multiphase Drives in Healthy and Faulty Modes
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DOI：10.1109/jestpe.2026.3687065.png)
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Abstract
En 中文
This article proposes a neural current controller for decentralized and nonsinusoidal permanent magnet multiphase drives. This architecture has some advantages, such as increased reliability and allows a simplified motor design, but requires new algorithms for decentralized control. The neural network current controller features a so-called “hybrid operation,” with one part trained offline and a second part working online. Using the machine’s analytical model, the network is first trained offline based on multiagent reinforcement learning (MARL) and is then implemented in the real-time control. In a second step, by using least mean square (LMS) algorithm, an online calibration is applied only to the output layer to take account the uncertainties of the model and allows at the same time reducing the computational burden. This solution offers a quick response thanks to the offline (forward) training and an adaptability of online (feedback) update. The proposal has been validated in simulation and in experimentation on a highly nonsinusoidal seven-phase test bench to demonstrate the feasibility of the proposed approach.
Keywords:
Adaline
current controller
decentralized
hybrid neural controller
least mean square (LMS)
multiphase machines
natural frame
neural networks
open-end winding
reinforcement learning (RL)
Journal
I
IF:
4.9
Papers:
249
Citations:
0
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Cited Papers