Aerial Robotic Manipulation

doi:10.1007/978-3-030-12945-3

Cited by 26 publications

(3 citation statements)

References 12 publications

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“…Newton's second law describes the relationship between the force F and the acceleration 𝑎experienced by a center of mass, with the three forces and the acceleration for each axis on the quadrotor, so the relationship is as shown in Eq. (6).…”

Section: Quadrotor Dynamics Modelmentioning

confidence: 99%

“…Quadrotor movement on the x, y, and z coordinate axes of the earth frame can be modeled with the rotation matrix of Eq. ( 5) and Newton II's law in Equation (6). The rotation matrix in the third column of Eq.…”

Section: Quadrotor Dynamics Modelmentioning

confidence: 99%

“…One of these UAVs is a quadrotor, which uses four motor actuators as a flight driver. More broadly, a quadrotor is a UAV that can take off and land vertically (VTOL) even in a limited area [6]. A quadrotor also has a constant flight speed to hover in a specific location with its stationary motion [7].…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Stability on Autonomous Waypoint Mission of Quadrotor Using LQR Integrator Control

Dhewa

Priyambodo

Nasuha

et al. 2022

IIUMEJ

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The ability of the quadrotor in the waypoint trajectory tracking becomes an essential requirement in the completion of various missions nowadays. However, the magnitude of steady-state errors and multiple overshoots due to environmental disturbances leads to motion instability. These conditions make the quadrotor experience a shift and even change direction from the reference path. As a result, to minimize steady-state error and multiple overshoots, this study employs a Linear Quadratic Regulator control method with the addition of an Integrator. Comparisons between LQR without Integrator and LQR with Integrator were performed. They were implemented on a quadrotor controller to track square and zig-zag waypoint patterns. From experimental results, LQR without Integrator produce of 2 meters steady-state error and -1.04 meters undershoot average with an accuracy of 64.84 % for square pattern, along 3.19 meters steady-state error, and -1.12 meters undershoot average with an accuracy of 46.73 % for a zig-zag way. The LQR method with integrator produce of 1.06 meters steady-state error with accuracy 94.96 % without multiple-overshoot for square pattern, the 1.06 meters steady-state error, and -0.18 meters undershoot average with an accuracy of 86.49 % for the zig-zag way. The results show that the LQR control method with Integrator can minimize and improve steady-state error and multiple overshoots in quadrotor flight. The condition makes the quadrotor able to flying path waypoints with the correct system specification. ABSTRAK: Kemampuan quadrotor dalam pengesanan lintasan waypoint menjadi syarat penting dalam menyelesaikan pelbagai misi pada masa kini. Walau bagaimanapun, besarnya ralat keadaan mantap dan banyak kelebihan kerana gangguan persekitaran menyebabkan ketidakstabilan pergerakan. Keadaan ini menjadikan quadrotor mengalami pergeseran dan bahkan mengubah arah dari jalur rujukan. Oleh itu, kajian ini menggunakan kaedah kawalan Linear Quadratic Regulator dengan penambahan integrator dalam meminimumkan ralat keadaan mantap dan banyak kelebihan. Perbandingan antara LQR tanpa Integrator dan LQR dengan Integrator dilakukan. Mereka dilaksanakan pada pengawal quadrotor untuk mengesan corak titik jalan persegi dan zig-zag. Dari hasil eksperimen, LQR tanpa Integrator menghasilkan ralat keadaan mantap 2 meter dan -1.04 meter rata-rata undur tembak dengan ketepatan 64.84% untuk corak persegi, sepanjang ralat keadaan tetap 3.19 meter, dan -1.12 meter rata-rata undur bawah dengan ketepatan 46.73 % untuk cara zig-zag. Kaedah LQR dengan integrator menghasilkan ralat keadaan mantap 1.06 meter dengan ketepatan 94.96% tanpa tembakan berlebihan untuk corak segi empat sama, ralat keadaan mantap 1.06 meter, dan rata-rata undur tembak -0.18 meter dengan ketepatan 86.49% untuk zig-zag cara. Hasilnya menunjukkan bahawa kaedah kawalan LQR dengan Integrator dapat meminimumkan dan memperbaiki ralat keadaan mantap dan banyak overhoot dalam penerbangan quadrotor. Keadaan tersebut menjadikan quadrotor dapat terbang ke titik jalan dengan spesifikasi sistem yang betul.

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Section: Quadrotor Dynamics Modelmentioning

confidence: 99%

Section: Quadrotor Dynamics Modelmentioning

confidence: 99%