• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.59-65
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• 2000

In this paper the comparison of the first order approximation schemes such as SLP(sequential linear programming) CONLIN(convex linearization) MMA(method of moving asymptotes) and the second order approximation scheme SQP(sequential quadratic programming) was accomplished for optimization of nonlinear structures. It was found that MMA and SQP are the most efficient methods for optimization. But the number of function call of SQP is much more than that of MMA. Therefore when it is considered with the expense of computation MMA is more efficient than SQP. In order to examine the efficiency of MMA for complex optimization problem it was applied to the helicopter tail boom con-sidering column buckling and local wall buckling constraints. it is concluded that MMA can be a very efficient approxima-tion scheme from simple problems to complex problems.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.58-66
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• 2009

Formation controller for multiple spacecrafts is designed based on a decentralized approach. The objective of the proposed controller is to make each spacecraft fly to the desired waypoints, while keeping the formation shape of multiple spacecrafts. To design the decentralized formation controller, the output feedback linearization technique using error functions for goal convergence and formation keeping is utilized for spacecraft dynamics. The primary contribution of this paper is to proposed optimal controller for formation flying based on the decentralized approach. To design the optimal controller, eigenvalue assignment technique is used. To verify the effectiveness of the proposed controller, numerical simulations are performed for three-dimensional waypoint-passing missions of multiple spacecrafts.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.133-142
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• 1999

We introduce the algorithms of 2-D and 3-D position estimation using 2-D vision sensors. The sensors used in this research issue red laser slit light to the body. So, it is very convenient to obtain the coordinates of corner point or edge in sensor coordinate. Since the measured points are normally not fixed in the body coordinate, the additional conditions, that corner lines or edges are straight and fixed in the body coordinate, are used to find out the position and orientation of the body. In the case of 2-D motional body, we can find the solution analytically. But in the case of 3-D motional body, linearization technique and least mean squares method are used because of hard nonlinearity.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.462-463
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• 2008

This paper proposes a method for dynamic output-feedback controller design for stochastic time-delay systems. Based on recent results on time-delay systems control, a tractable and delay-dependent design condition is proposed, which provides a dynamic output-feedback controller to render the closed-loop stochastic time-delay systems to be asymptotically stable in the mean-square sense. The feasibility problem of the proposed condition is recast into a cone complementarity problem. An algorithm adopting cone complementarity linearization is presented to solve the resulting problem.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.439-442
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• 2000

An adaptive nonlinear control of a brushless direct drive motor(BLDDM) is proposed. Comparing to the traditional PMSM the direct drive motor has smaller number of per pole and per phase slots to provide higher torque in low speed. This generic construction generates flux harmonics and finally results in unwanted torque harmonics. To control the speed a feedback linearization method is applied by choosing the $i_{ds}$ and $\omega_{m}$ as the output variables. The control of the flux harmonics is provided by using a flux observer with MRAC technique. As shown in the simula-tion results the proposed nonlinear speed controller has a good speed response in the steady state and robust to the flux variation

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1444-1448
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• 2010

The ball and beam system is one of the most popular models for studying control systems because of its nonlinearity and several control techniques have been proposed. Sliding mode control is a popular robust control method which rejects the external disturbance. In this paper, we propose a robust controller using the ${\epsilon}$-sliding surface. On the ${\epsilon}$-sliding surface, the system robustness and convergence can be manipulated via a use of ${\epsilon}$. We show the stability analysis and convergence analysis on the ${\epsilon}$-sliding surface. In addition, the experimental results show the validity of the proposed controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.102-108
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• 2008

This paper presents robust and adaptive control method for complicated three dimensional crane systems with uncertain effect. We consider an overhead crane system in which a trolly located on its top is moved to x- and y-axis independently. We first approximate the complicated crane model through linearization approach to simply construct a PD control and then design an adaptive control system for compensating modeling error and control deviation which is feasibly occurred due to system perturbation in practice. An adaptive control scheme is analytically derived using Lyapunov stability theory for a given bound of system perturbation. We accomplish numerical simulation for evaluation of the proposed control system and demonstrate its superiority comparing with the traditional control strategy.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.238-242
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• 2013

We consider the problem of constructing observers for nonlinear systems with unknown inputs. Connectionist networks, also called neural networks, have been broadly applied to solve many different problems since McCulloch and Pitts had shown mathematically their information processing ability in 1943. In this thesis, we present a genetic neuro-control scheme for nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.534-537
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• 1995

An open loop vibrational control of underactuated mechanical system with amplitude and frequency modulations is investigated. The underactuated systems sonsidered in the paper are assumed to have free joints with no brake. The active joints are positioned first by a linearizing control, and then periodic oscillatory input are applied to them to move the remaining free joints to their desired states. A systematic way of obtaining averaged systems for the underactuated systems with oscillatory vibration is developed. A complete solution to the open loop control strateegy in terms of determining amplitudes and frequencies for general system is still under investigation. However, a specific control design for 2R manipulator which is obtained through the averaged system is demonstrated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.