In this research, a single-axis attitude control test bed is developed, and simulation and tests experiments are performed, as a preliminary research of a quad-rotor aerial vehicle development. A single-axis test bed with seesaw configuration is manufactured using two motors and propellers, and the aerodynamic parameters are derived by thrust tests. The response of the system is estimated with Matlab/Simulink, and experiments are performed with attitude control computer and an attitude sensor onboard the test bed. Comparing the results of simulated and tested data, factors of steady-state errors during experiments are found, and performances of used attitude control algorithm and the control computer were verified. In these process, essential preliminary data for attitude control of a quad-rotor unmanned aerial vehicle were acquired.
This paper describes the design and implementation of a network system for UAV for multiple clients that enables long-range operation based on a commercial mobile network. A prototype data modem is developed with a commercial embedded M2M module in order to provide an access to the mobile network. A central server with a database is constructed to record all of real-time flight and video data and communicate with a ground control system. A GCS is developed for the central control, the single UAV and the smart phone version to be used for different purposes. Performance tests were progressed for data delay, video frame rate and state of clients. Flight tests were also performed to verify the reliability of the modem with respect to altitude.
This paper describes a result of the guidance and control for re-entry vehicle during TAEM phase. TAEM phase (Terminal Aerial Energy Management phase) has many conditions, such as density, velocity, and so on. Under these conditions, we have optimized trajectory and other states for guidance in TAEM phase. Optimized states consist of 7 variables, down range, cross range, altitude, velocity, flight path angle, vehicle's azimuth and flight range. For optimizing, we dealt with DIDO as a programming tool, and we used feedback linearization with neural network for control re-entry vehicle. By back propagation algorithm, vehicle dynamics is approximated to real. New command can be decided using the approximated dynamics, delayed command input and plant output, control system. Using this control law, we tracked 7 states, made by optimization.
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