A New Vibration Controller Design Method Using Reinforcement Learning and FIR Filters: A Numerical and Experimental Study Article Swipe
Xingxing Feng
,
Hong Chen
,
Gang Wu
,
Anfu Zhang
,
Zhigao Zhao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12199869
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12199869
High-dimensional high-frequency continuous-vibration control problems often have very complex dynamic behaviors. It is difficult for the conventional control methods to obtain appropriate control laws from such complex systems to suppress the vibration. This paper proposes a new vibration controller by using reinforcement learning (RL) and a finite-impulse-response (FIR) filter. First, a simulator with enough physical fidelity was built for the vibration system. Then, the deep deterministic policy gradient (DDPG) algorithm interacted with the simulator to find a near-optimal control policy to meet the specified goals. Finally, the control policy, represented as a neural network, was run directly on a controller in real-world experiments with high-dimensional and high-frequency dynamics. The simulation results show that the maximum peak values of the power-spectrum-density (PSD) curves at specific frequencies can be reduced by over 63%. The experimental results show that the peak values of the PSD curves at specific frequencies were reduced by more than 47% (maximum over 52%). The numerical and experimental results indicate that the proposed controller can significantly attenuate various vibrations within the range from 50 Hz to 60 Hz.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app12199869
- https://www.mdpi.com/2076-3417/12/19/9869/pdf?version=1665292761
- OA Status
- gold
- Cited By
- 5
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4303578950
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- OpenAlex ID
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https://openalex.org/W4303578950Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app12199869Digital Object Identifier
- Title
-
A New Vibration Controller Design Method Using Reinforcement Learning and FIR Filters: A Numerical and Experimental StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-30Full publication date if available
- Authors
-
Xingxing Feng, Hong Chen, Gang Wu, Anfu Zhang, Zhigao ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3390/app12199869Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/12/19/9869/pdf?version=1665292761Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/12/19/9869/pdf?version=1665292761Direct OA link when available
- Concepts
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Reinforcement learning, Control theory (sociology), Controller (irrigation), Vibration, Finite impulse response, Computer science, Vibration control, Range (aeronautics), Engineering, Acoustics, Control (management), Algorithm, Physics, Artificial intelligence, Biology, Aerospace engineering, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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