Speed Optimization Control of a Permanent Magnet Synchronous Motor Based on TD3 Article Swipe
Zuolei Hu
,
Yingjie Zhang
,
Ming Li
,
Yuhua Liao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/en18040901
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/en18040901
Permanent magnet synchronous motors (PMSMs) are widely used in industrial automation and electric vehicles due to their high efficiency and excellent dynamic performance. However, controlling PMSMs presents challenges such as parameter variations and system nonlinearities. This paper proposes a twin delayed deep deterministic policy gradient (TD3)-based energy-saving optimization control method for PMSM drive systems. The TD3 algorithm uses double networks, target policy smoothing regularization, and delayed actor network updates to improve training stability and accuracy. Simulation experiments under two operating conditions show that the TD3 algorithm outperforms traditional proportional–integral (PI) controllers and linear active disturbance rejection control (LADRC) controllers in terms of reference trajectory tracking, q-axis current regulation, and speed tracking error minimization. The results demonstrate the TD3 algorithm’s effectiveness in enhancing motor efficiency and system robustness, offering a novel approach to PMSM drive system control through deep reinforcement learning.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en18040901
- OA Status
- gold
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407534111
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407534111Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en18040901Digital Object Identifier
- Title
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Speed Optimization Control of a Permanent Magnet Synchronous Motor Based on TD3Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-13Full publication date if available
- Authors
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Zuolei Hu, Yingjie Zhang, Ming Li, Yuhua LiaoList of authors in order
- Landing page
-
https://doi.org/10.3390/en18040901Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/en18040901Direct OA link when available
- Concepts
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Magnet, Permanent magnet synchronous motor, Control (management), Control theory (sociology), Automotive engineering, Synchronous motor, Permanent magnet synchronous generator, Computer science, Electrical engineering, Engineering, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.linear | 92 |
| abstract_inverted_index.magnet | 1 |
| abstract_inverted_index.method | 49 |
| abstract_inverted_index.motors | 3 |
| abstract_inverted_index.policy | 43, 61 |
| abstract_inverted_index.q-axis | 105 |
| abstract_inverted_index.system | 33, 125, 134 |
| abstract_inverted_index.target | 60 |
| abstract_inverted_index.widely | 6 |
| abstract_inverted_index.(LADRC) | 97 |
| abstract_inverted_index.(PMSMs) | 4 |
| abstract_inverted_index.control | 48, 96, 135 |
| abstract_inverted_index.current | 106 |
| abstract_inverted_index.delayed | 40, 65 |
| abstract_inverted_index.dynamic | 21 |
| abstract_inverted_index.improve | 70 |
| abstract_inverted_index.network | 67 |
| abstract_inverted_index.results | 114 |
| abstract_inverted_index.through | 136 |
| abstract_inverted_index.updates | 68 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.approach | 130 |
| abstract_inverted_index.electric | 12 |
| abstract_inverted_index.gradient | 44 |
| abstract_inverted_index.offering | 127 |
| abstract_inverted_index.presents | 26 |
| abstract_inverted_index.proposes | 37 |
| abstract_inverted_index.systems. | 53 |
| abstract_inverted_index.tracking | 110 |
| abstract_inverted_index.training | 71 |
| abstract_inverted_index.vehicles | 13 |
| abstract_inverted_index.Permanent | 0 |
| abstract_inverted_index.accuracy. | 74 |
| abstract_inverted_index.algorithm | 56, 85 |
| abstract_inverted_index.enhancing | 121 |
| abstract_inverted_index.excellent | 20 |
| abstract_inverted_index.learning. | 139 |
| abstract_inverted_index.networks, | 59 |
| abstract_inverted_index.operating | 79 |
| abstract_inverted_index.parameter | 30 |
| abstract_inverted_index.reference | 102 |
| abstract_inverted_index.rejection | 95 |
| abstract_inverted_index.smoothing | 62 |
| abstract_inverted_index.stability | 72 |
| abstract_inverted_index.tracking, | 104 |
| abstract_inverted_index.Simulation | 75 |
| abstract_inverted_index.automation | 10 |
| abstract_inverted_index.challenges | 27 |
| abstract_inverted_index.conditions | 80 |
| abstract_inverted_index.efficiency | 18, 123 |
| abstract_inverted_index.industrial | 9 |
| abstract_inverted_index.trajectory | 103 |
| abstract_inverted_index.variations | 31 |
| abstract_inverted_index.(TD3)-based | 45 |
| abstract_inverted_index.controllers | 90, 98 |
| abstract_inverted_index.controlling | 24 |
| abstract_inverted_index.demonstrate | 115 |
| abstract_inverted_index.disturbance | 94 |
| abstract_inverted_index.experiments | 76 |
| abstract_inverted_index.outperforms | 86 |
| abstract_inverted_index.regulation, | 107 |
| abstract_inverted_index.robustness, | 126 |
| abstract_inverted_index.synchronous | 2 |
| abstract_inverted_index.traditional | 87 |
| abstract_inverted_index.optimization | 47 |
| abstract_inverted_index.performance. | 22 |
| abstract_inverted_index.algorithm’s | 118 |
| abstract_inverted_index.deterministic | 42 |
| abstract_inverted_index.effectiveness | 119 |
| abstract_inverted_index.energy-saving | 46 |
| abstract_inverted_index.minimization. | 112 |
| abstract_inverted_index.reinforcement | 138 |
| abstract_inverted_index.nonlinearities. | 34 |
| abstract_inverted_index.regularization, | 63 |
| abstract_inverted_index.proportional–integral | 88 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.77949096 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |