• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.96-103
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• 2023

This paper presents a robust position tracking controller for a motor used in semiconductor equipment, utilizing the motor angle measurement. Precise position control is challenging due to the presence of uncertainties in various motor applications. The proposed controller consists of a PD (Proportional-Derivative) controller and a PIO (Proportional-Integral Observer) to estimate the system's state and equivalent disturbance compensating for the uncertainties. Since the stability alternates as the observer gain increases, we have investigated it through the closedloop root locus under the system parameters change. The analysis has showed that the inertia of the motor is the main parameter that affects it, and by adjusting the control gain appropriately, the system can be rendered to be stable even when the inertia of the motor changes. The effectiveness of the proposed control algorithm is validated through computer simulations, followed by a comparison of its performance with the results of a previous study.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.6
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• pp.648-657
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• 2014

This paper is about the study on a newly developed small waterproofed 4-axis robot arm and the analysis of its kinematics and dynamics. The structure of robot arm is designed to have Pitch-Pitch-Pitch-Yaw joint motion for inspection using a camera on itself and the joint actuator driving capacity are selected and the joint actuators are designed and test for 10m waterproofness. The closed-form solution for the robot arm is derived through the forward and inverse kinematics analysis. Also, the dynamics model equation including the damping force due to the mechanical seal for waterproofness is derived using Newton-Euler method. Using derived dynamics equation, a sliding mode controller is designed to track the desired path of the developed robot arm, and its performance is verified through a simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.747-752
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• 2006

In this paper, the adaptive fuzzy sliding mode controller with two systems is designed. The existing sliding mode controller used to $approximation{\^{u}}(t)$ with discrete sgn function and sat function for keeping the state trajectories on the sliding surface[1]. The proposed controller decrease the disturbance for uncertain control gain and This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems ate used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties ate demonstrated. Futhermore, fuzzy tuning improve tracking abilities by changing some sliding conditions. In the traditional sliding mode control, ${\eta}$ is a positive constant. The increase of ${\eta}$ has led to a significant decrease in the rise time. However, this has resulted in higher overshoot. Therefore the proposed ${\eta}$ tuning AFSMC improve the performances, so that the controller can track the trajectories faster and more exactly than ordinary controller. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.48-57
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• 2014

It is suggested that the curvature trajectory be used to estimate the real-time moving sound sources and efficiently the robot estimating the sound sources. Since the target points of the real-time moving sound sources change, the mobile robot continuously estimates the changed target points. In such a case, the robot experiences a slip phenomenon due to the abnormal velocity and the changes of the navigating state. By selecting an appropriate curvature and navigating the robot gradually by using it, it is possible to enable the robot to reach the target points without having much trouble. In order to recognize the sound sources in real time, three microphones need to be organized in a straight form. Also, by applying the cross-correlation algorithm to the TDOA base, the signals can be analyzed. By using the analyzed data, the locations of the sound sources can be recognized. Based on such findings, the sound sources can be estimated. Even if the mobile robot is navigated by selecting the gradual curvature based on the changed target points, there could be errors caused by the inertia and the centrifugal force related to the velocity. As a result, it is possible to control the velocity of both wheels of the robot through the velocity PID controller in order to compensate for the slip phenomenon and minimize the estimated errors. In order to examine whether the suggested curvature trajectory is appropriate for estimating the sound sources, two mobile robots are arranged to carry out an actual experiment. The first robot is moved by discharging the sound sources, while the second robot recognizes and estimates the locations of the discharged sound sources in real time.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.2
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• pp.84-91
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• 2005

For long time, the takeoff and landing control of airship was worked by human handling. With the development of the autonomous control system, the exact controls during the takeoff and landing were required and lots of methods and algorithms were suggested. This paper presents the result of airship take-off and landing by buoyancy control using air ballonet volume change and performance control of pitch angle for stable flight within the desired altitude. For the complexity of airship's dynamics, firstly, simple PID controller was applied. Due to the various atmospheric conditions, this controller didn't give satisfactory results. Therefore, new control method was designed to reduce rapidly the error between designed trajectory and actual trajectory by learning algorithm using an artificial neural network. Generally, ANN has various weaknesses such as large training time, selection of neuron and hidden layer numbers required to deal with complex problem. To overcome these drawbacks, in this paper, the RBFN (radial basis function network) controller developed. The weight value of RBFN is acquired by learning which to reduce the error between desired input output through and airship dynamics to impress the disturbance. As a result of simulation, the controller using the RBFN is superior to PID controller which maximum error is 15M.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.1
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• pp.56-76
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• 2024

We for the first time made a successful longest continuous sectional observation in the East Sea by an underwater glider during 95 days from September 18 to December 21 2020 in the Korea along the 106 Line (129.1 °E ~ 131.5 °E at 37.9 °N) of the regular shipboard measurements by the National Institute of Fishery Science (NIFS) and obtained twelve hydrographic sections with high spatiotemporal resolution. The glider was deployed at 129.1 °E in September 18 and conducted 88-days flight from September 19 to December 15 2020, yielding twelve hydrographic sections, and then recovered at 129.2 °E in December 21 after the last 6 days virtual mooring operation. During the total traveled distance of 2550 km, the estimated deviation from the predetermined zonal path had an average RMS distance of 262 m. Based on these high-resolution long-term glider measurements, we conducted a comparative study with the bi-monthly NIFS measurements in terms of spatial and temporal resolutions, and found distinguished features. One is that spatial features of sub-mesoscale such as sub-mesoscale frontal structure and intensified thermocline were detected only in the glider measurements, mainly due to glider's high spatial resolution. The other is the detection of intramonthly variations from the weekly time series of temperature and salinity, which were extracted from glider's continuous sections. Lastly, there were deviations and bias in measurements from both platforms. We argued these deviations in terms of the time scale of variation, the spatial scale of fixed-point observation, and the calibration status of CTD devices of both platforms.