PID gain plot for <i>ψ</i> dynamics.
<div><p>This paper presents a novel hybrid combined neural network and fuzzy logic adaptive proportional, integral, and derivative(NNPID+FPID) control strategy that integrates neural networks and fuzzy logic for optimizing Unmanned Aerial Vehicle(UAV) dynamics by tuning the gains of a PI...
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| منشور في: |
2025
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| _version_ | 1852017163781537792 |
|---|---|
| author | Nigatu Wanore Madebo (22146141) |
| author_facet | Nigatu Wanore Madebo (22146141) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nigatu Wanore Madebo (22146141) |
| dc.date.none.fl_str_mv | 2025-08-29T17:43:58Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0331036.g014 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/PID_gain_plot_for_i_i_dynamics_/30013566 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Biotechnology Developmental Biology Infectious Diseases Biological Sciences not elsewhere classified Information Systems not elsewhere classified proposed approach leverages mean squared error layer neural network integrates neural networks combining neural networks applying neural networks >&# 981 ;</ >&# 968 ;</ >&# 952 ;</ 10 hidden neurons dimensional control challenges states using proportional fuzzy logic enhances div >< p derivative errors () adjust pid gains fuzzy logic pid controller input errors control strategy work demonstrates time adaptability remaining states paper presents ensure real |
| dc.title.none.fl_str_mv | PID gain plot for <i>ψ</i> dynamics. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>This paper presents a novel hybrid combined neural network and fuzzy logic adaptive proportional, integral, and derivative(NNPID+FPID) control strategy that integrates neural networks and fuzzy logic for optimizing Unmanned Aerial Vehicle(UAV) dynamics by tuning the gains of a PID controller. The proposed approach leverages the strengths of each technique by applying neural networks to fine-tune the <i>y</i> and <i>ψ</i> states, while fuzzy logic enhances the performance of <i>x</i>, <i>z</i>, <i>ϕ</i>, and <i>θ</i> dynamics. A single-layer neural network with 10 hidden neurons is utilized to adjust PID gains for the <i>y</i> and <i>ψ</i> states using proportional, integral, and derivative errors () as inputs. The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. Concurrently, fuzzy logic employs heuristic rules to dynamically tune PID gains for the remaining states, based on input errors and their derivatives. Membership functions map inputs to gains to ensure real-time adaptability. The hybrid method outperforms standalone neural network(NNPID) and fuzzy logic(FPID) approaches by significantly improving trajectory tracking performance and overall UAV control efficiency. This work demonstrates the effectiveness of combining neural networks and fuzzy logic to address the multi-dimensional control challenges of UAV systems.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_4e8765dfd41c36a4731c96b0ca6bcc60 |
| identifier_str_mv | 10.1371/journal.pone.0331036.g014 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30013566 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | PID gain plot for <i>ψ</i> dynamics.Nigatu Wanore Madebo (22146141)NeuroscienceBiotechnologyDevelopmental BiologyInfectious DiseasesBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedproposed approach leveragesmean squared errorlayer neural networkintegrates neural networkscombining neural networksapplying neural networks>&# 981 ;</>&# 968 ;</>&# 952 ;</10 hidden neuronsdimensional control challengesstates using proportionalfuzzy logic enhancesdiv >< pderivative errors ()adjust pid gainsfuzzy logicpid controllerinput errorscontrol strategywork demonstratestime adaptabilityremaining statespaper presentsensure real<div><p>This paper presents a novel hybrid combined neural network and fuzzy logic adaptive proportional, integral, and derivative(NNPID+FPID) control strategy that integrates neural networks and fuzzy logic for optimizing Unmanned Aerial Vehicle(UAV) dynamics by tuning the gains of a PID controller. The proposed approach leverages the strengths of each technique by applying neural networks to fine-tune the <i>y</i> and <i>ψ</i> states, while fuzzy logic enhances the performance of <i>x</i>, <i>z</i>, <i>ϕ</i>, and <i>θ</i> dynamics. A single-layer neural network with 10 hidden neurons is utilized to adjust PID gains for the <i>y</i> and <i>ψ</i> states using proportional, integral, and derivative errors () as inputs. The weights are updated through a gradient descent algorithm minimizing the mean squared error, with a nonlinear sigmoid activation function ensuring adaptability. Concurrently, fuzzy logic employs heuristic rules to dynamically tune PID gains for the remaining states, based on input errors and their derivatives. Membership functions map inputs to gains to ensure real-time adaptability. The hybrid method outperforms standalone neural network(NNPID) and fuzzy logic(FPID) approaches by significantly improving trajectory tracking performance and overall UAV control efficiency. This work demonstrates the effectiveness of combining neural networks and fuzzy logic to address the multi-dimensional control challenges of UAV systems.</p></div>2025-08-29T17:43:58ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0331036.g014https://figshare.com/articles/figure/PID_gain_plot_for_i_i_dynamics_/30013566CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300135662025-08-29T17:43:58Z |
| spellingShingle | PID gain plot for <i>ψ</i> dynamics. Nigatu Wanore Madebo (22146141) Neuroscience Biotechnology Developmental Biology Infectious Diseases Biological Sciences not elsewhere classified Information Systems not elsewhere classified proposed approach leverages mean squared error layer neural network integrates neural networks combining neural networks applying neural networks >&# 981 ;</ >&# 968 ;</ >&# 952 ;</ 10 hidden neurons dimensional control challenges states using proportional fuzzy logic enhances div >< p derivative errors () adjust pid gains fuzzy logic pid controller input errors control strategy work demonstrates time adaptability remaining states paper presents ensure real |
| status_str | publishedVersion |
| title | PID gain plot for <i>ψ</i> dynamics. |
| title_full | PID gain plot for <i>ψ</i> dynamics. |
| title_fullStr | PID gain plot for <i>ψ</i> dynamics. |
| title_full_unstemmed | PID gain plot for <i>ψ</i> dynamics. |
| title_short | PID gain plot for <i>ψ</i> dynamics. |
| title_sort | PID gain plot for <i>ψ</i> dynamics. |
| topic | Neuroscience Biotechnology Developmental Biology Infectious Diseases Biological Sciences not elsewhere classified Information Systems not elsewhere classified proposed approach leverages mean squared error layer neural network integrates neural networks combining neural networks applying neural networks >&# 981 ;</ >&# 968 ;</ >&# 952 ;</ 10 hidden neurons dimensional control challenges states using proportional fuzzy logic enhances div >< p derivative errors () adjust pid gains fuzzy logic pid controller input errors control strategy work demonstrates time adaptability remaining states paper presents ensure real |