A fuzzy neural network approach for predicting and optimizing the dynamic stiffness of the Stewart platform Article Swipe
Zhiqiang Zhao
,
Yuetao Liu
,
Changsong Yu
,
Peicen Jiang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10791-025-09786-w
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10791-025-09786-w
This study proposes a fuzzy neural network-based prediction and optimization method to address the challenge of modeling dynamic stiffness in Stewart platforms. Traditional approaches, such as the Newton-Euler method and finite element analysis, often struggle to capture nonlinear characteristics and multivariate coupling effects under complex conditions. To overcome these limitations, this paper constructs a fuzzy neural network framework that integrates fuzzy logic with neural computation. This model selects drive joint position, velocity, acceleration, torque, and external load as input variables. These inputs are mapped into fuzzy subsets through fuzzification. The fuzzy radial basis function network is designed to simulate the nonlinear relationships between input variables and dynamic stiffness. An error back-propagation algorithm is applied to optimize the network weights, and the structure is refined using cross-validation and grid search. The fuzzy rule base is constructed from both expert knowledge and data-driven insights. Experimental validation is conducted under varying working conditions. This includes load variation and angular velocity changes. The proposed method demonstrates higher accuracy and robustness compared to traditional Newton-Euler, finite element, statistical regression, and reinforcement learning models. The average mean square error under most scenarios is significantly reduced. This paper also highlights the limitations of current fuzzy rule adaptability under unknown disturbances. Future work aims to enhance model generalizability through self-learning mechanisms and simplify computational complexity for real-time applications. Overall, this study contributes a reliable and adaptive approach to improving dynamic stiffness prediction for Stewart platforms, offering insights for broader applications in multi-degree-of-freedom robotic systems.
Related Topics
Concepts
Fuzzy logic
Robustness (evolution)
Artificial neural network
Nonlinear system
Computer science
Fuzzy rule
Neuro-fuzzy
Adaptability
Radial basis function
Artificial intelligence
Fuzzy control system
Control theory (sociology)
Engineering
Generalizability theory
Adaptive neuro fuzzy inference system
Machine learning
Mean squared error
Membership function
Stiffness
Finite element method
Machine tool
Control engineering
Multivariate statistics
Data mining
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10791-025-09786-w
- OA Status
- diamond
- References
- 28
- OpenAlex ID
- https://openalex.org/W7106550429
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106550429Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10791-025-09786-wDigital Object Identifier
- Title
-
A fuzzy neural network approach for predicting and optimizing the dynamic stiffness of the Stewart platformWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-11-24Full publication date if available
- Authors
-
Zhiqiang Zhao, Yuetao Liu, Changsong Yu, Peicen JiangList of authors in order
- Landing page
-
https://doi.org/10.1007/s10791-025-09786-wPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1007/s10791-025-09786-wDirect OA link when available
- Concepts
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Fuzzy logic, Robustness (evolution), Artificial neural network, Nonlinear system, Computer science, Fuzzy rule, Neuro-fuzzy, Adaptability, Radial basis function, Artificial intelligence, Fuzzy control system, Control theory (sociology), Engineering, Generalizability theory, Adaptive neuro fuzzy inference system, Machine learning, Mean squared error, Membership function, Stiffness, Finite element method, Machine tool, Control engineering, Multivariate statistics, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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28Number of works referenced by this work
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| abstract_inverted_index.framework | 58 |
| abstract_inverted_index.improving | 231 |
| abstract_inverted_index.insights. | 142 |
| abstract_inverted_index.knowledge | 139 |
| abstract_inverted_index.nonlinear | 38, 101 |
| abstract_inverted_index.position, | 71 |
| abstract_inverted_index.real-time | 219 |
| abstract_inverted_index.scenarios | 186 |
| abstract_inverted_index.stiffness | 19, 233 |
| abstract_inverted_index.structure | 122 |
| abstract_inverted_index.variables | 105 |
| abstract_inverted_index.variation | 154 |
| abstract_inverted_index.velocity, | 72 |
| abstract_inverted_index.complexity | 217 |
| abstract_inverted_index.constructs | 53 |
| abstract_inverted_index.highlights | 193 |
| abstract_inverted_index.integrates | 60 |
| abstract_inverted_index.mechanisms | 213 |
| abstract_inverted_index.platforms, | 237 |
| abstract_inverted_index.platforms. | 22 |
| abstract_inverted_index.prediction | 8, 234 |
| abstract_inverted_index.robustness | 166 |
| abstract_inverted_index.stiffness. | 108 |
| abstract_inverted_index.validation | 144 |
| abstract_inverted_index.variables. | 80 |
| abstract_inverted_index.Traditional | 23 |
| abstract_inverted_index.approaches, | 24 |
| abstract_inverted_index.conditions. | 46, 150 |
| abstract_inverted_index.constructed | 135 |
| abstract_inverted_index.contributes | 224 |
| abstract_inverted_index.data-driven | 141 |
| abstract_inverted_index.limitations | 195 |
| abstract_inverted_index.regression, | 174 |
| abstract_inverted_index.statistical | 173 |
| abstract_inverted_index.traditional | 169 |
| abstract_inverted_index.Experimental | 143 |
| abstract_inverted_index.Newton-Euler | 28 |
| abstract_inverted_index.adaptability | 200 |
| abstract_inverted_index.applications | 242 |
| abstract_inverted_index.computation. | 65 |
| abstract_inverted_index.demonstrates | 162 |
| abstract_inverted_index.limitations, | 50 |
| abstract_inverted_index.multivariate | 41 |
| abstract_inverted_index.optimization | 10 |
| abstract_inverted_index.Newton-Euler, | 170 |
| abstract_inverted_index.acceleration, | 73 |
| abstract_inverted_index.applications. | 220 |
| abstract_inverted_index.computational | 216 |
| abstract_inverted_index.disturbances. | 203 |
| abstract_inverted_index.network-based | 7 |
| abstract_inverted_index.reinforcement | 176 |
| abstract_inverted_index.relationships | 102 |
| abstract_inverted_index.self-learning | 212 |
| abstract_inverted_index.significantly | 188 |
| abstract_inverted_index.fuzzification. | 89 |
| abstract_inverted_index.characteristics | 39 |
| abstract_inverted_index.back-propagation | 111 |
| abstract_inverted_index.cross-validation | 126 |
| abstract_inverted_index.generalizability | 210 |
| abstract_inverted_index.multi-degree-of-freedom | 244 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |