Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms
Abstract The use of multi-winged drones in real-world applications continues to make it a topic of scholarly research. This paper considers the stabilization of the quadcopter’s attitude rates along the three axes in a static wind gust environment. The determination of suitable proportional integral...
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Discover Electronics |
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| Online Access: | https://doi.org/10.1007/s44291-025-00049-y |
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| author | Olukunle Kolawole Soyinka Monica Ngunan Ikpaya Lumi Luka |
| author_facet | Olukunle Kolawole Soyinka Monica Ngunan Ikpaya Lumi Luka |
| author_sort | Olukunle Kolawole Soyinka |
| collection | DOAJ |
| description | Abstract The use of multi-winged drones in real-world applications continues to make it a topic of scholarly research. This paper considers the stabilization of the quadcopter’s attitude rates along the three axes in a static wind gust environment. The determination of suitable proportional integral and derivative (PID) gain parameters for a controller is often an arduous task and can depend on manual cut and try methods and extensive experience. This study solves this problem using Bio-inspired algorithms to tune the controller gain. To determine the required PID controller gain parameters this paper utilizes a Simulink model of a quadcopter combined with the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm (CSA) optimization respectively to minimize error in the attitude rate. Comparison is made of the gain parameters of the PID controller obtained from the two Bio-inspired algorithms implemented to stabilize the attitude rate of a quadcopter unmanned aerial vehicle. The convergence rate, final error and thrust input from the motors are used as metric for comparison of the two algorithms. The results indicate that the two algorithms performed suitably with the PSO showing 2.85E−07, 2.89E−08 and 1.45E−07 as the convergence error rate while CSA indicated 1.80E−08, 4.51E−09 and 3.34E−09 for the roll. Pitch and yaw axes respectively this compares well with −3.3570E−04, −1.0268E−04, −3.8474E−07 obtained by manual tuning. Cuckoo search indicated marginal improvement in the final error rate compared to the PSO algorithm. |
| format | Article |
| id | doaj-art-d4c6928f8c054fc5a976f6aa422cba4d |
| institution | OA Journals |
| issn | 2948-1600 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Electronics |
| spelling | doaj-art-d4c6928f8c054fc5a976f6aa422cba4d2025-08-20T02:15:08ZengSpringerDiscover Electronics2948-16002025-02-012111910.1007/s44291-025-00049-yStudy on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithmsOlukunle Kolawole Soyinka0Monica Ngunan Ikpaya1Lumi Luka2Engineering & Space Systems Department, National Space Research & Development AgencyEngineering & Space Systems Department, National Space Research & Development AgencyEngineering & Space Systems Department, National Space Research & Development AgencyAbstract The use of multi-winged drones in real-world applications continues to make it a topic of scholarly research. This paper considers the stabilization of the quadcopter’s attitude rates along the three axes in a static wind gust environment. The determination of suitable proportional integral and derivative (PID) gain parameters for a controller is often an arduous task and can depend on manual cut and try methods and extensive experience. This study solves this problem using Bio-inspired algorithms to tune the controller gain. To determine the required PID controller gain parameters this paper utilizes a Simulink model of a quadcopter combined with the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm (CSA) optimization respectively to minimize error in the attitude rate. Comparison is made of the gain parameters of the PID controller obtained from the two Bio-inspired algorithms implemented to stabilize the attitude rate of a quadcopter unmanned aerial vehicle. The convergence rate, final error and thrust input from the motors are used as metric for comparison of the two algorithms. The results indicate that the two algorithms performed suitably with the PSO showing 2.85E−07, 2.89E−08 and 1.45E−07 as the convergence error rate while CSA indicated 1.80E−08, 4.51E−09 and 3.34E−09 for the roll. Pitch and yaw axes respectively this compares well with −3.3570E−04, −1.0268E−04, −3.8474E−07 obtained by manual tuning. Cuckoo search indicated marginal improvement in the final error rate compared to the PSO algorithm.https://doi.org/10.1007/s44291-025-00049-yPSOCuckoo searchUAVOptimizationPID |
| spellingShingle | Olukunle Kolawole Soyinka Monica Ngunan Ikpaya Lumi Luka Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms Discover Electronics PSO Cuckoo search UAV Optimization PID |
| title | Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms |
| title_full | Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms |
| title_fullStr | Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms |
| title_full_unstemmed | Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms |
| title_short | Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms |
| title_sort | study on pid gain parameter optimization for a quadcopter under static wind turbulence using bio inspired algorithms |
| topic | PSO Cuckoo search UAV Optimization PID |
| url | https://doi.org/10.1007/s44291-025-00049-y |
| work_keys_str_mv | AT olukunlekolawolesoyinka studyonpidgainparameteroptimizationforaquadcopterunderstaticwindturbulenceusingbioinspiredalgorithms AT monicangunanikpaya studyonpidgainparameteroptimizationforaquadcopterunderstaticwindturbulenceusingbioinspiredalgorithms AT lumiluka studyonpidgainparameteroptimizationforaquadcopterunderstaticwindturbulenceusingbioinspiredalgorithms |