Iza, Belgis Ainatul (2026) Pengembangan Model Matematika dan Perancangan Kontrol pada Quadcopter. Doctoral thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Estimasi state yang akurat merupakan faktor kunci dalam pengendalian quadcopter, terutama pada kondisi sensor yang tidak ideal akibat gangguan pengukuran seperti bias, drift, dan outlier. Pendekatan Fuzzy-PID berbasis Extended Kalman Filter (EKF) telah banyak digunakan, namun umumnya belum mempertimbangkan kompensasi gross error secara eksplisit sehingga kinerja kontrol dapat menurun. Penelitian ini mengusulkan arsitektur kendali robust dengan mengintegrasikan Measurement-Compensated Extended Kalman Filter (MC-EKF) ke dalam pengendali Fuzzy-PID adaptif. MC-EKF menggabungkan deteksi gross error, estimasi besaran error, dan kompensasi pengukuran secara langsung dalam proses estimasi state, sehingga parameter Fuzzy-PID disetel berdasarkan data yang telah dikoreksi. Model dinamika quadcopter 6-DOF diturunkan menggunakan formulasi Newton–Euler dengan mempertimbangkan efek aerodinamik dan giroskopik.Hasil simulasi menunjukkan bahwa pada kondisi tanpa gross error, sistem mencapai settling time 0,1193 s dengan overshoot 0,0024% dan RMSE posisi kurang dari 0,5. Pada kondisi gangguan pengukuran berupa bias 0,1, drift 0,0001, dan outlier amplitudo 10, sistem tetap stabil dengan settling time masing-masing 7,281 s, 8,133 s, dan 5,219 s pada sumbu-x, y, dan z. Seluruh state tetap konvergen menuju referensi meskipun terjadi peningkatan RMSE pada kanal tertentu. Hasil ini menegaskan bahwa integrasi MC-EKF meningkatkan robustness pengendali Fuzzy-PID secara signifikan terhadap gangguan pengukuran ekstrem.
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Accurate state estimation is a key factor in quadcopter control, particularly under non-ideal sensor conditions caused by measurement disturbances such as bias, drift, and outliers. Extended Kalman Filter (EKF)-based Fuzzy-PID controllers have been widely adopted; however, most existing approaches do not explicitly address gross error compensation, which may lead to degraded control performance.This study proposes a robust control architecture by integrating a Measurement-Compensated Extended Kalman Filter (MC-EKF) into an adaptive Fuzzy-PID controller. The MC-EKF incorporates gross error detection, error magnitude estimation, and measurement compensation directly into the state estimation process, ensuring that the Fuzzy-PID parameters are tuned based on corrected measurements. A six-degree-of-freedom (6-DOF) quadcopter dynamic model is derived using the Newton–Euler formulation while accounting for aerodynamic and gyroscopic effects.Simulation results show that under gross error-free conditions, the system achieves a settling time of 0.1193 s with an overshoot of 0.0024% and a positional RMSE below 0.5. Under measurement disturbances consisting of a bias of 0.1, a drift of 0.0001, and outliers with an amplitude of 10, the system remains stable with settling times of 7.281 s, 8.133 s, and 5.219 s along the x-, y-, and z-axes, respectively. All state variables converge to their reference trajectories despite increased RMSE in certain channels.These results confirm that the integration of MC-EKF significantly enhances the robustness of the Fuzzy-PID controller against severe measurement disturbances.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Quadcopter, extended kalman filter, measurement compensation, gross error, fuzzy-PID. |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA401 Mathematical models. Q Science > QA Mathematics > QA402 System analysis. Q Science > QA Mathematics > QA402.3 Kalman filtering. Q Science > QA Mathematics > QA9.64 Fuzzy logic T Technology > TJ Mechanical engineering and machinery > TJ217 Adaptive control systems T Technology > TJ Mechanical engineering and machinery > TJ217.2 Robust control T Technology > TJ Mechanical engineering and machinery > TJ223 PID controllers |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44002-(S3) PhD Thesis |
| Depositing User: | Belgis Ainatul Iza |
| Date Deposited: | 10 Feb 2026 03:42 |
| Last Modified: | 10 Feb 2026 03:42 |
| URI: | http://repository.its.ac.id/id/eprint/132305 |
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