gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems

Balakrishna, Praneeth; Book, Gerrit; Kirchgässner, Wilhelm; Schenke, Maximilian; Traue, Arne; Wallscheid, Oliver

doi:10.21105/joss.02498

Cited by 16 publications

(12 citation statements)

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“…The training and testing programs are implemented in Python 3.7. The drive system is simulated using the GEM library [23], the setup and training of the DQN is handled with the use of kerasRL [30], kerasRL2 [31] and tensorflow 2 [32].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…with the d and q inductances L d , L q , the stator resistance R s , the permanent magnetic flux ψ p , the pole-pair number p , the mechanical angular velocity ω me , the stator current i s and the generated drive torque T . This model will be utilized for the simulative analysis, which is conducted based on the open-source control simulation software gym-electric-motor (GEM) [23].…”

Section: A Permanent Magnet Synchronous Motormentioning

confidence: 99%

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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%

Section: A Permanent Magnet Synchronous Motormentioning

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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“…Current control in electrical drives is a typical intermediate control problem that is to be solved to enable utilization within cascaded controllers for torque or speed control tasks. The corresponding drive environment is simulated using the opensource gym-electric-motor (GEM) [41] library. During the training process, the reference values are drawn randomly at each sampling instant using the built-in reference generator.…”

Section: B Electric Motor Applicationmentioning

confidence: 99%

Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control

Weber¹,

Schenke²,

Wallscheid³

2022

Preprint

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Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Where classical control approaches require a priori system knowledge, data-driven control approaches like RL allow a modelfree controller design procedure, rendering them emergent techniques for systems with changing plant structures and varying parameters. While it was already shown in various applications that the transient control behavior for complex systems can be sufficiently handled by RL, the challenge of non-vanishing steady-state control errors remains, which arises from the usage of control policy approximations and finite training times. To overcome this issue, an integral action state augmentation (IASA) for actor-critic-based RL controllers is introduced that mimics an integrating feedback, which is inspired by the delta-input formulation within model predictive control. This augmentation does not require any expert knowledge, leaving the approach model free. As a result, the RL controller learns how to suppress steady-state control deviations much more effectively. Two exemplary applications from the domain of electrical energy engineering validate the benefit of the developed method both for reference tracking and disturbance rejection. In comparison to a standard deep deterministic policy gradient (DDPG) setup, the suggested IASA extension allows to reduce the steady-state error by up to 52 % within the considered validation scenarios.

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“…8(a) and underlines the potential of datadriven controller design. To accelerate the development and training of RL-agents for electric motor control, an opensource gym-electric-motor (GEM) Python toolbox is published in [68], [69] that contains models of different dc and threephase motor variants for easily accessible simulation. This package can be readily used to compare the trained RL agents with other state-of-the-art control approaches.…”

Section: A Workflow From Simulation To the Test Benchmentioning

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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gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems

Cited by 16 publications

References 0 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

Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control

Artificial Intelligence in Electric Machine Drives: Advances and Trends

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