Rotation-matrix-based attitude control without angular velocity measurements

2014 ◽  
Author(s):  
David Evan Zlotnik ◽  
James Richard Forbes
Keyword(s):  
Angular Velocity ◽  
Attitude Control ◽  
Rotation Matrix ◽  
Velocity Measurements

scholarly journals Velocity-Free Attitude Control of Quadrotors: A Nonlinear Negative Imaginary Approach

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2387
Author(s):  
Ahmed G. Ghallab ◽  
Ian R. Petersen
Keyword(s):  
Angular Velocity ◽  
Attitude Control ◽  
Velocity Measurements ◽  
External Disturbances ◽  
New Approach ◽  
Control Approach ◽  
Modeling Uncertainties ◽  
The Face ◽  
Robust Asymptotic Stability ◽  
Stability Results

In this paper, we propose a new approach to the attitude control of quadrotors, by which angular velocity measurements or a model-based observer reconstructing the angular velocity are not needed. The proposed approach is based on recent stability results obtained for nonlinear negative imaginary systems. In specific, through an inner-outer loop method, we establish the nonlinear negative imaginary property of the quadrotor rotational subsystem. Then, a strictly negative imaginary controller is synthesized using the nonlinear negative imaginary results. This guarantees the robust asymptotic stability of the attitude of the quadrotor in the face of modeling uncertainties and external disturbances. First simulation results underline the effectiveness of the proposed attitude control approach are presented.

scholarly journals Attitude Control of Quadrotor Using PD Plus Feedforward controller on SO(3)

2018 ◽  
Vol 8 (1) ◽  
pp. 566 ◽  
Author(s):  
Almido H Ginting ◽  
Oyas Wahyunggoro ◽  
Adha Imam Cahyadi
Keyword(s):  
Angular Velocity ◽  
Attitude Control ◽  
Rotation Matrix ◽  
Identity Matrix ◽  
Simple Scheme ◽  
Error Matrix ◽  
Rotation Error ◽  
Error Variable ◽  
Feedforward Controller

<p>This paper proposes a simple scheme of Proportional-Derivative (PD) plus Feedforward controller on SO(3) to control the attitude of a quadrotor. This controller only needs the measurement of angular velocity to calculate the exponential coordinates of the rotation matrix. With rotation matrix as an error variable of the controller, the simulation shows that the controller is able to drive the attitude of the quadrotor from hovering condition to desired attitude and from an attitude condition goes to the hovering condition, despite the system is disturbed. When the system is convergent, the rotation error matrix will be a 3x3 identity matrix.</p>

scholarly journals Implementation of the centralized control system for drone training

2018 ◽  
Vol 7 (3.3) ◽  
pp. 379
Author(s):  
Bong Hyun Kim
Keyword(s):  
Control System ◽  
Angular Velocity ◽  
Embedded System ◽  
Flight Control ◽  
Attitude Control ◽  
Test System ◽  
Measurement Unit ◽  
Centralized Control ◽  
Structural Problems ◽  
Earth Magnetic Field

Background/Objectives: The drones became a representative item in the IoT era. However, there is no drone pilot test system that can safely train this in the education field. Drones have very dangerous structural problems, so it is very necessary to practice them easily. Therefore, it is necessary to develop a system that can control the drones safely and easily while controlling them.Methods/Statistical analysis: In this paper, we will develop software for controlling a dedicated board platform that can securely perform ground testing by mounting four drones of motor and drive on a board (PCB). To this end, we supported various control IMU (Inertial Measurement Unit) boards for attitude control by using sensor which is the core technology of drone flight control. Also, Acceleration Data, Angular Velocity Data, Earth Magnetic Field Data, and Atmospheric Pressure Data for maintaining the altitude were used for the drone flight.Findings: In the implemented central control system, the AT chip is built in and designed to perform all control related to the flight of the drone. In addition, since it is an embedded system, we have programmed the attitude control using the sensor, the motor output setting, and the controller connection information. The CPU required for drones control can be replaced with various types of controllers besides Fno Arduino, UNO, Muiltiwii. For this purpose, the main PCB is designed so that the power supply terminal can be used for each CPU. Finally, it was developed as a setup program to correct the sensor and output of the drone.Improvements/Applications: The system implemented in this paper can easily control the drone. In addition, acceleration, angular velocity, geomagnetic field, air pressure sensor, GPS, etc. necessary for drone control can be utilized by stabilizing the initial set value. In other words, the zero point of the sensor can be captured and the signal appropriate to the current state of the drone can be stored in the processor.  

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