Attitude and altitude tracking of hexacopter via LQR with integral action

Suicmez, Emre Can; Kutay, Ali Türker

doi:10.1109/icuas.2017.7991327

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…It has been mentioned that the tracking position error can be translated to the tracking altitude error z e and the rotational angular error η e shown in (7). z e has been discussed and converged to zero under the controller PL-SMC, and the desired attitude…”

Section: B Desired Attitude Calculationmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Yue Zhang. predictive control [6], linear quadratic regulator [7], proportional integral derivative control [8], fuzzy control [9], [10], back stepping control [11], sliding mode control and adaptive control [12]- [14]. It became clear that attenuation control strategies and disturbance observer can effectively eliminate the influence of external disturbances and model uncertainties to ensure good attitude tracking performance [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

et al. 2019

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Unmanned aerial vehicles (UAVs) at low altitude flight may significantly degrade their performance and the safety under wind disturbances and incorrect operations. This paper presents a robust control strategy for UAVs to achieve good performance of low altitude flight and disturbance rejection. First, a novel second-order hexacopter dynamics is established and the position tracking is translated to the altitude and the rotational angle tracking problem. An integrated control scheme is created to deal with the challenges faced by hexacopter at low altitude flight, in which the influence of near-ground threshold distance and the desired roll, pitch, and yaw are analyzed. Moreover, an improved flying altitude planner and an attitude planner for low altitude conditions are designed respectively to avoid the overturning risk due to the big reaction torque and external disturbances. Second, a sliding-mode-based altitude tracking controller and an attitude tracking controller are designed to reduce the tracking errors and improve the robustness of the system. Finally, the proposed control scheme is tested on simulation and experiment platforms of multi-rotor UAV to show the feasibility and accurate trajectory tracking at low altitude flight. INDEX TERMS Modeling, tracking control, low altitude, hexacopter, robustness. YUQING CHEN received the master's and Ph.D. degrees in control theory and control engineering from the

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Section: B Desired Attitude Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

et al. 2019

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“…Beberapa metode kendali telah banyak diajukan pada sistem kendali quadrotor. Di antara metode kendali tersebut adalah metode Proportional Integral Derivatif (PID) [5], Fuzzy, Ant Colony Optimization [6], dan Full State Feedback dengan Linier Quadratic Regulator (LQR) [7]. Metode metode full state feedback LQR merupakan salah satu metode kendali yang banyak digunakan di dalam dunia kendali UAV secara umum.…”

Section: Pendahuluanunclassified

“…Fungsi aktivasi sigmoid tersebut digunakan untuk menjaga nilai masing-masing node pada setiap lapisan agar tidak berlebih dan masih saling berkolerasi pada setiap lapisan. Sistem jaringan saraf ini menggunakan inisialisasi bobot awal seperti pada (7). Sedangkan untuk nilai bobot jaringan saraf dari hidden layer ke output layer digunakan nilai awal konstanta K hasil dari penalaan menggunakan metode LQR.…”

Section: B Sistem Jaringan Saraf Tiruanunclassified

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

Rahani

Priyambodo

2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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One type of unmanned aircraft that is often used today is quadrotor. This type of aircraft has the ability to take off vertically. This study implemented an altitude control system on the z-axis quadrotor. The control used is the full state method of Linear Quadratic Regulator (LQR) with artificial neural networks. The LQR full state feedback method used in this system is 12-states with each feedback constant K tuned to the neural network method. This study implements the artificial neural network method to change the feedback constant on the z-axis. Artificial neural network architecture used 12 input layers, 48 hidden layers, and 1 output layer. This study compares the value of the results of the simulation with the response value of the system implementation results applied to the quadrotor. Testing with full state LQR feedback using artificial neural networks improves the system response to ±0.77 seconds and improves steady state error values up to ±12 cm. Based on the results of these studies, this system can be implemented to control other systems. Intisari-Salah satu jenis pesawat tanpa awak yang sering digunakan saat ini adalah quadrotor. Pesawat jenis ini memiliki kemampuan untuk lepas landas secara vertikal. Makalah ini mengimplementasikan sistem kendali ketinggian pada sumbu z quadrotor. Kendali yang digunakan yaitu metode full state feedback Linear Quadratic Regulator (LQR) dengan jaringan saraf tiruan. Metode full state feedback LQR yang digunakan pada sistem ini adalah dua belas state dengan masing-masing konstanta feedback K ditala dengan metode jaringan saraf tiruan. Makalah ini mengimplementasikan metode jaringan saraf tiruan untuk mengubah konstanta feedback pada sumbu z. Arsitektur jaringan saraf tiruan yang digunakan yaitu dua belas input layer, 48 hidden layer, dan satu output layer. Makalah ini membandingkan nilai hasil simulasi dengan nilai respons sistem hasil implementasi yang diterapkan pada quadrotor. Pengujian dengan full state feedback LQR menggunakan jaringan saraf tiruan memperbaiki respons sistem hingga ±0,77 detik serta perbaikan nilai steady state error hingga ±12 cm. Berdasarkan hasil penelitian tersebut, sistem ini dapat diimplementasikan kendali pada sistem yang lain.

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“…The full-state Linear-Quadratic Regulator (LQR) controller was successfully applied for trajectory tracking for various aircraft: fixed-wing [6,8,9], helicopters [10,11] and UAVs [12,13]. In case of incomplete state information, the LQG controller can be used instead.…”

Section: Introductionmentioning

confidence: 99%

System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

2020

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This paper presents a controller design process for an aircraft tracking problem when not all states are available. In the study, a nonlinear-transport aircraft simulation model was used and identified through Maximum Likelihood Principle and Extended Kalman Filter. The obtained mathematical model was used to design a Linear–Quadratic Regulator (LQR) with optimal weighting matrices when not all states are measured. The nonlinear aircraft simulation model with LQR controller tracking abilities were analyzed for multiple experiments with various noise levels. It was shown that the designed controller is robust and allows for accurate trajectory tracking. It was found that, in ideal atmospheric conditions, the tracking errors are small, even for unmeasured variables. In wind presence, the tracking errors were proportional to the wind velocity and acceptable for small and moderate disturbances. When turbulence was present in the experiment, state variable oscillations occurred that were proportional to the turbulence intensity and acceptable for small and moderate disturbances.

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Attitude and altitude tracking of hexacopter via LQR with integral action

Cited by 9 publications

References 9 publications

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

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