• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.249-254
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• 2009

This paper studies the problem of pole placement by an LQ controller for system having two distinct real poles. Using the so-called Pole's Moving Range (PMR) drawn in the s-plane and relational equations between closed-loop system poles and weighting matrices, we calculate the state weighting matrix to move two distinct real poles to a pair of complex poles. By numerical examples, we show that the proposed method is applied to improve system performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1066-1073
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• 2014

This paper presents a new design method of optimal control of system which are changed the system parameters. The method used for this purpose are the Lagrange interpolation method and Pole's Moving range method. We selects a system within the scope of the changing the system parameters. Using pole's moving range we calculated the state weighting matrix of optimal control. The optimal controller is designed by Lagrange interpolation method of the state weighting matrix. We are compared with a traditional optimal controller and proposed method by simulation. The simulation showed that the proposed method is better control performance than traditional method of optimal controller.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.574-580
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• 2007

This paper deals with LQR controller design method tor system having complex poles. The proposed method is capable of systematically calculating weighting matrices based on the pole's moving-range and the relational equation between closed-loop pole(s) and weighting matrices. The method moves complex poles to complex poles or two distinct real poles. This will provide much-needed functionality to apply LQR controller. The example shows the feasibility of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.864-869
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• 2005

This paper proposes a new method for LQR controller design. It is unsystematic and difficult to design an LQR controller by trial and error. The proposed method is capable of systematically calculating weighting matrices for desired pole(s) by the pole's moving-range in S-plane and the relational equation between closed-loop pole(s) and weighting matrices. This will provide much-needed functionality to apply LQR controller. The example shows the feasibility of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.20-27
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• 2020

In general, a nonlinear system is linearized in the form of a multiplication of the 1st and 2nd order system. This paper reports a design method of a weighting matrix and control law of LQ control to move the double poles that have a Jordan block to a pair of complex conjugate poles. This method has the advantages of pole placement and the guarantee of stability, but this method cannot position the poles correctly, and the matrix is chosen using a trial and error method. Therefore, a relation function (𝜌, 𝜃) between the poles and the matrix was derived under the condition that the poles are the roots of the characteristic equation of the Hamiltonian system. In addition, the Pole's Moving-range was obtained under the condition that the state weighting matrix becomes a positive semi-definite matrix. This paper presents examples of how the matrix and control law is calculated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.608-616
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• 2018

If a general nonlinear system is linearized by the successive multiplication of the 1st and 2nd order systems, then there are four types of poles in this linearized system: the pole of the 1st order system and the equal poles, two distinct real poles, and complex conjugate pair of poles of the 2nd order system. Linear Quadratic (LQ) control is a method of designing a control law that minimizes the quadratic performance index. It has the advantage of ensuring the stability of the system and the pole placement of the root of the system by weighted matrix adjustment. LQ control by the weighted matrix can move the position of the pole of the system arbitrarily, but it is difficult to set the weighting matrix by the trial and error method. This problem can be solved using the characteristic equations of the Hamiltonian system, and if the control weighting matrix is a symmetric matrix of constants, it is possible to move several poles of the system to the desired closed loop poles by applying the control law repeatedly. The paper presents a method of calculating the state weighting matrix and the control law for moving the equal poles with Jordan blocks to two real poles using the characteristic equation of the Hamiltonian system. We express this characteristic equation with a state weighting matrix by means of a trigonometric function, and we derive the relation function (${\rho},\;{\theta}$) between the equal poles and the state weighting matrix under the condition that the two real poles are the roots of the characteristic equation. Then, we obtain the moving-range of the two real poles under the condition that the state weighting matrix becomes a positive semi-finite matrix. We calculate the state weighting matrix and the control law by substituting the two real roots selected in the moving-range into the relational function. As an example, we apply the proposed method to a simple example 3rd order system.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.279-288
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• 2004

This paper presents a novel design for a tubular linear brushless permanent-magnet motor. In this design, the magnets in the moving part are oriented in an NS-NS―SN-SN fashion which leads to higher magnetic force near the like-pole region. An analytical methodology to calculate the motor force and to size the actuator was developed. The linear motor is operated in conjunction with a position sensor, three power amplifiers, and a controller to form a complete solution for controlled precision actuation. Real-time digital controllers enhanced the dynamic performance of the motor, and gain scheduling reduced the effects of a nonlinear dead band. In its current state, the motor has a rise time of 30 ms, a settling time of 60 ms, and 25% overshoot to a 5-mm step command. The motor has a maximum speed of 1.5 m/s and acceleration up to 10 g. It has a 10-cm travel range and 26-N maximum pull-out force. The compact size of the motor suggests it could be used in robotic applications requiring moderate force and precision, such as robotic-gripper positioning or actuation. The moving part of the motor can extend significantly beyond its fixed support base. This reaching ability makes it useful in applications requiring a small, direct-drive actuator, which is required to extend into a spatially constrained environment.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.9
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• pp.431-437
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• 2001

Since the eddy current problem usually depends on the geometry of the moving conductive sheet and the shape of the pole projection area, there is no general method to find out its analytical solution. The analysis of the eddy current in a rotating disk is performed in the case of time-invariant field to find its analytical solution. As a method to solve the eddy current problem, the concept of the Coulomb charge and image method are proposed with the consideration of the boundary condition. Firstly, the line charge is obtained from the volume charge generated in the rotating disk and Coulomb's law is applied. Secondly, the finite disk radius is considered by introducing an imaginary eddy current to satisfy the boundary condition that the radial component of the eddy current is zero at the edge of the relating disk. Thirdly, the braking torque is calculated by applying Lorentz force law. Finally, the computed braking torque is compared with the measured one As a result, it can be said that the proposed model presents fairly accurate results in a low angular velocity range although a large error is observed as the angular velocity of the disk increases.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1299-1310
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• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.