PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal

Rajesh, R J; Ananda, CM

doi:10.1109/icpace.2015.7274930

Cited by 27 publications

(23 citation statements)

References 1 publication

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“…is perpendicular to the plane , pointing to the right [24]. In the attitude data packet, the north, east, and downward speed curves can be found.…”

Section: Speed Coordinate Systemsmentioning

confidence: 99%

“…The origin is taken at the center of gravity of the aircraft, and the axis O a X a is in the same direction as the flight speed V; the axis O a Z a is located on the vertical axis O a X a of the plane of symmetry of the aircraft, pointing to the belly; O a Y a is perpendicular to the plane X a O a Z a , pointing to the right [24]. In the attitude data packet, the north, east, and downward speed curves can be found.…”

Section: Speed Coordinate Systemsmentioning

confidence: 99%

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Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Xiong

et al. 2019

Electronics

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The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

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“…is perpendicular to the plane , pointing to the right [24]. In the attitude data packet, the north, east, and downward speed curves can be found.…”

Section: Speed Coordinate Systemsmentioning

confidence: 99%

Section: Speed Coordinate Systemsmentioning

confidence: 99%

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Xiong

et al. 2019

Electronics

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“…clockwise dan counter clockwise untuk sudut pitch dan 10° clockwise dan counter clockwise untuk sudut roll [7].…”

Section: Analisis Sistemunclassified

Sistem Kendali Gimbal 2-Sumbu Sebagai Tempat Kamera Pada Quadrotor Menggunakan PID Fuzzy

Haris¹,

Dharmawan

Atmaji

2017

IJEIS (Indones. J. Electr. Instrum. Syst.)

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“…Hal ini dikarenakan metode kendali PID ini mempunyai struktur yang sederhana, mudah untuk dipelihara dan mudah untuk di tala ulang [6]. Selain itu, metode system kendali PID juga dapat digabungkan dengan algoritme lain seperti algoritme Ant Colony Optimization [8] [9], Particle Swarm Optimization [10], fuzzy [11], neural network [12] dan algoritme-algoritme lainnya yang hingga saat ini masih terus dikembangkan [13].…”

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Implementasi Sistem Kendali PID pada Gimbal Kamera 2-sumbu dengan Aktuator Motor Brushless

Priyambodo¹

2017

IJEIS (Indones. J. Electr. Instrum. Syst.)

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PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal

Cited by 27 publications

References 1 publication

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Sistem Kendali Gimbal 2-Sumbu Sebagai Tempat Kamera Pada Quadrotor Menggunakan PID Fuzzy

Implementasi Sistem Kendali PID pada Gimbal Kamera 2-sumbu dengan Aktuator Motor Brushless

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