Deep Learning-Based Long-Horizon MPC: Robust, High Performing, and Computationally Efficient Control for PMSM Drives-Reference-Cited by-同舟云学术

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Deep Learning-Based Long-Horizon MPC: Robust, High Performing, and Computationally Efficient Control for PMSM Drives

Published:2022-10 Issue:10 Volume:37 Page:12486-12501
ISSN:0885-8993
Container-title:IEEE Transactions on Power Electronics
language:
Short-container-title:IEEE Trans. Power Electron.

Author:

Abu-Ali Mohammad¹^ORCID,Berkel Felix²^ORCID,Manderla Maximilian²^ORCID,Reimann Sven²^ORCID,Kennel Ralph¹^ORCID,Abdelrahem Mohamed³^ORCID

Affiliation:

1. Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich, Munich, Germany

2. Corporate Research of Robert Bosch GmbH, Renningen, Germany

3. Chair of High-Power Converter Systems (HLU), Technical University of Munich, Munich, Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/63/9803836/09769875.pdf?arnumber=9769875

Reference58 articles.

1. Long-Horizon Direct Model Predictive Control Based on Neural Networks for Electrical Drives

2. A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

3. Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning

4. Near-Optimal Rapid MPC Using Neural Networks: A Primal-Dual Policy Learning Framework

5. Accuracy and computational efficiency of suboptimal nonlinear predictive control based on neural models

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