Perancangan Sistem Kontrol LPV-MPC Pada Mobil Otonom Berbasis Robot Operating System Untuk Menghindari Obstacle Dinamis

Muarif, Moh. Idris (2026) Perancangan Sistem Kontrol LPV-MPC Pada Mobil Otonom Berbasis Robot Operating System Untuk Menghindari Obstacle Dinamis. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Tingginya angka kecelakaan lalu lintas akibat kelalaian manusia (44,33%) mendorong urgensi inovasi teknologi mobil otonom. Tantangan fundamental dalam pengembangannya adalah merancang sistem kontrol yang presisi sekaligus mampu menghindari rintangan dinamis secara real-time tanpa memicu beban komputasi berlebih. Penelitian ini mengusulkan arsitektur Cascade Control berbasis Multiple-Input Multiple-Output (MIMO) menggunakan metode Linear Parameter Varying - Model Predictive Control (LPV-MPC). Sistem dibagi menjadi dua lapisan komputasi: Outer Loop (Kinematic LPV-MPC) yang beroperasi pada 10 Hz untuk kalkulasi target kecepatan dan laju putar secara taktis, serta Inner Loop (Dynamic LPV-LQR) pada 100 Hz untuk eksekusi aktuator fisik. Algoritma ini disimulasikan menggunakan model Tesla Model 3 pada lingkungan CARLA yang terintegrasi dengan Robot Operating System 2 (ROS 2). Inovasi kalkulasi Continuous Cross-Track Error diterapkan untuk mengeliminasi penyimpangan osilasi setir ekstrem. Hasil simulasi menunjukkan kendaraan sukses mencapai margin galat lateral statis yang sangat stabil sebesar ±0.05 meter. Pada skenario penghindaran halangan dinamis, sistem berhasil mengeksekusi manuver S-Curve menyalip kendaraan lain melalui mekanisme Virtual Reference Injection (y_{offset}=2.8 meters). Hal ini dibuktikan dengan adanya lonjakan galat lateral terkontrol sebesar -5.20 m sebagai kompromi keselamatan absolut. Transformasi arsitektur ini terbukti sangat efisien, di mana penggunaan solver OSQP sukses mencetak waktu komputasi rata-rata 0.21 milidetik, beroperasi jauh lebih cepat dari batas latensi kritis 10 ms. Penelitian ini berkontribusi langsung pada inovasi mobilitas cerdas dan keselamatan bertransportasi berkelanjutan.
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The high rate of traffic accidents caused by human error (44.33%) drives the urgency for autonomous vehicle technology innovation. The fundamental challenge in its development is designing a precise control system capable of avoiding dynamic obstacles in real-time without triggering excessive computational loads. This study proposes a Multiple-Input Multiple-Output (MIMO) Cascade Control architecture using the Linear Parameter Varying - Model Predictive Control (LPV-MPC) method. The system is divided into two computational layers: the Outer Loop (Kinematic LPV-MPC) operating at 10 Hz for tactical target speed and yaw rate calculations, and the Inner Loop (Dynamic LPV-LQR) at 100 Hz for physical actuator execution. The algorithm is simulated using a Tesla Model 3 model in the CARLA environment integrated with the Robot Operating System 2 (ROS 2). Continuous Cross-Track Error calculation innovation is applied to eliminate extreme steering oscillation anomalies. Simulation results show the vehicle successfully achieves a highly stable static lateral error margin of ±0.05 meters. In the dynamic obstacle avoidance scenario, the system successfully executes an S-Curve maneuver to overtake other vehicles through a Virtual Reference Injection mechanism (y_{offset}=2.8 meter). This is evidenced by a controlled lateral error spike of -5.20 m as an absolute safety trade-off. This architectural transformation proves highly efficient, where the OSQP solver successfully records an average computation time of 0.21 milliseconds, operating much faster than the 10 ms critical latency limit. This research directly contributes to smart mobility innovation and sustainable transportation safety.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Kendaraan Otonom, Kontrol Kaskade, LPV-MPC, Smart City (SDG 9), Traffic Safety (SDG 3), Autonomous Vehicle, Cascade Control, LPV-MPC
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles.
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Moh. Idris Muarif
Date Deposited:	28 Jul 2026 06:19
Last Modified:	28 Jul 2026 06:19
URI:	http://repository.its.ac.id/id/eprint/138657

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