Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept

Schenke, Maximilian; Kirchgässner, Wilhelm; Wallscheid, Oliver

doi:10.1109/tii.2019.2948387

Cited by 66 publications

(28 citation statements)

References 16 publications

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“…The exploring starts are important to cover the entire drive's operation range during training. The model parameters used in (19) are unknown to the DQ-DTC agent and were only used to narrow down the training to the relevant working areas of the operation range.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors

Schenke

Wallscheid

2021

IEEE Open J. Ind. Electron. Soc.

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Section: Simulation Resultsmentioning

confidence: 99%

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors

Schenke

Wallscheid

2021

IEEE Open J. Ind. Electron. Soc.

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“…They can be used to design the whole electrical drive systems, including both electrical machines and their power electronics, and control systems. Recently, DL has been successfully employed to design the controller to drive the electrical machines [154,155]. As there are many types of control algorithms, such as field-oriented control, direct torque control, and model predictive control, more research work shall be conducted.…”

Section: Machine Learning For System-level Design Optimization Of Elementioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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“…Additionally, there is a wide variety of deep RL algorithms and high flexibility at the implementation level, such as the design of state space, the action space, and the reward function, etc. Despite their widespread applications in AlphaGo, robots, and self-driving cars, RL has only fairly recently been introduced to the control electric machines [67]- [77].…”

Section: Deep Reinforcement Learning-enabled Next Generation Electric Machine Drivesmentioning

confidence: 99%

“…However, the entire field of electric machine drives remains pretty much silent on the resurgence of AI in this deep 1 learning era, when compared with its continued success and widespread application in condition monitoring [38]- [44], design optimization [45]- [64], and manufacturing [65], [66] of various types of electric machines. It wasn't until in the last few years that research efforts have begun to gradually catch up with the trend [67]- [77]. It is anticipated that with the rapid progress in deep learning models and hardware embedded platforms, AI-based data-driven approaches will become increasingly popular for the high-performance control of electric machine drives, as envisioned in Fig.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Zhang¹

2021

Preprint

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This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.

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Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept

Cited by 66 publications

References 16 publications

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Artificial Intelligence in Electric Machine Drives: Advances and Trends

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