• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.201-206
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• 2016

Aircraft have a cross axis of the three each other for maintenance of aircraft position. It is called roll axis, pitch axis and yaw axis. Attitude reference system is a sensor for detecting a change of the three axis. In this paper, mobile robot platform install part of Pan-tilt and HMD attitude reference system, because of we use control camera. The acceleration sensor is very weak a lot of noise to vibration, also problem with data from process of mapping to the data problems to arise. However to solve this problem, we removed the average filter and Cosine Interpolation for Pan-tilt. Using capacity evaluation for outdoor environment for we are proposing. Mobile robot has HMD and equipped Pan-tilt. We control mobile robot camera. In this experiment result is little bit delay happening, however Pan-tilt camera is relatively stable control checking. Also, we will checking any terrain and slopes is no problem for mobile robot driving skills.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.3
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• pp.127-134
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• 2003

In this paper, the speed control problem of moving camera is investigated for tracking the movement of the human-face. The camera system with pan-tilt mechanism sends an image to PC and PC sends back tracking coordinate to the camera. Then the camera tracks a human face in real time. The speed of the stepping motors for moving the camera must be controlled to the target region fast enough and smoothly, In this paper, a fuzzy logic controller is proposed for driving step motors. By creating driving acceleration and deceleration speed Profile, the speed of the motors is controled fast and smoothly. Experiments are performed to show the effectiveness of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.2
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• pp.125-131
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• 2017

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.