Desain Sistem Kontrol Fuzzy-MPC Untuk Trajectory Tracking Pada Hexacopter Dengan Adaptasi Terhadap Gangguan Angin

Ginting, Sundy Petro Batra (2026) Desain Sistem Kontrol Fuzzy-MPC Untuk Trajectory Tracking Pada Hexacopter Dengan Adaptasi Terhadap Gangguan Angin. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Hexacopter merupakan salah satu jenis UAV (Unmanned Aerial Vehicle) yang memiliki kemampuan lepas landas dan mendarat vertikal serta stabilitas yang baik dalam membawa beban. Namun, dalam pengoperasiannya, hexacopter rentan terhadap gangguan lingkungan seperti angin yang menyebabkan turbulensi dan penurunan performa UAV. Pada penelitian ini dibangun sistem kontrol MPC (Model Predictive Control) dan FLC (Fuzzy Logic Control) untuk meningkatkan performa trajectory tracking serta ketahanan sistem terhadap gangguan eksternal. MPC akan diterapkan sebagai kontrol outer loop yang berfungsi untuk mengatur posisi dan kecepatan translasi hexacopter, sedangkan kontrol inner loop menggunakan kontrol PID berbasis PX4. MPC akan menentukan percepatan translasi berdasarkan posisi dan kecepatan translasi, yang kemudian diubah menjadi referensi attitude dan thrust. MPC diintegrasikan dengan sistem guidance ILOS course control yang berfungsi untuk menghasilkan referensi lintasan dan kecepatan yang lebih tahan terhadap gangguan angin. FLC dibangun untuk mengubah nilai weight function Q dan R berdasarkan error posisi dan kecepatan translasi hexacopter. Sistem yang telah dibangun divalidasi menggunakan program simulasi PX4 SITL (Software In The Loop) berbasis ROS1 Noetic dan Gazebo Classic. Hasil pengujian menunjukkan bahwa sistem Fuzzy-MPC mampu mencapai akurasi posisi yang lebih tinggi dibandingkan metode MPC pada bidang horizontal untuk seluruh jenis lintasan. Pada bidang horizontal, Fuzzy-MPC menghasilkan Cross Track Error (CTE) yang lebih kecil sebesar 75.308% pada lintasan persegi, 42.810% pada lintasan lingkaran, 17.580% pada lintasan heliks, dan 1.912% pada lintasan belah ketupat. Pada bidang vertikal, Fuzzy-MPC lebih baik sebesar 48.841% pada lintasan persegi dan 24.318% pada lintasan lingkaran, namun lebih buruk sebesar 14.258% pada lintasan heliks dan 74.279% pada lintasan belah ketupat. Berdasarkan uji paired t-test dengan batas signifikansi 0.05, perbedaan performa yang signifikan ditemukan pada lintasan persegi dan lingkaran untuk kedua bidang serta lintasan heliks pada bidang horizontal, sedangkan pada lintasan heliks bidang vertikal dan lintasan belah ketupat data yang dimiliki belum menunjukkan adanya perbedaan yang signifikan.
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A hexacopter is a type of UAV (Unmanned Aerial Vehicle) known for its vertical takeoff and landing capabilities, as well as its excellent stability when carrying payloads. However, during operation, hexacopters are highly vulnerable to environmental disturbances like wind, which can cause turbulence and degrade UAV performance. In this study, a control system combining MPC (Model Predictive Control) and FLC (Fuzzy Logic Control) was developed to improve trajectory tracking performance and enhance the system’s resilience against external disturbances. MPC is implemented as the outer-loop control to regulate the hexacopter’s position and translational velocity, while the inner-loop control utilizes a PX4-based PID controller. The MPC determines the translational acceleration based on the position and translational velocity, which is then converted into attitude and thrust references. Additionally, the MPC is integrated with an ILOS (Integral Line-of-Sight) course control guidance system, which generates path and velocity references that are more robust against wind disturbances. The FLC is designed to dynamically tune the weight functions Q and R based on the hexacopter’s position and translational velocity errors. The developed system was validated using the PX4 SITL (Software In The Loop) simulation platform based on ROS1 Noetic and Gazebo Classic. The experimental results demonstrate that the Fuzzy-MPC system achieves higher positioning accuracy compared to the standard MPC on the horizontal plane for all trajectory types. On the horizontal plane, Fuzzy-MPC produces a smaller Cross Track Error (CTE) by 75.308% for the square trajectory, 42.810% for the circle trajectory, 17.580% for the helix trajectory, and 1.912% for the diamond trajectory. On the vertical axis, Fuzzy-MPC performs better by 48.841% for the square trajectory and 24.318% for the circle trajectory, but performs worse by 14.258% for the helix trajectory and 74.279% for the diamond trajectory. Based on a paired t-test with a 0.05 significance level, a significant performance difference is found for the square and circle trajectories on both planes as well as the helix trajectory on the horizontal plane, whereas for the helix trajectory on the vertical axis and the diamond trajectory the available data does not yet show a significant

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Hexacopter, Model Predictive Control, Fuzzy Logic Controller, Integral Line-of-Sight Guidance, Gangguan Angin, PX4,Hexacopter, Model Predictive Control, Fuzzy Logic Controller, Integral Line-of-Sight Guidance, Wind Disturbance, PX4
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles. U Military Science > UG1242 Drone aircraft--Control systems. (unmanned vehicle)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Sundy Petro Batra Ginting
Date Deposited:	27 Jul 2026 07:46
Last Modified:	27 Jul 2026 07:46
URI:	http://repository.its.ac.id/id/eprint/137980

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