• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.753-770
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• 1996

In order to reduce the number of rehandles during the loading operation of export containers in port container terminals, the storage location of each arriving container should be determined considering of its weight. We formulate the problem by a dynamic programming model to get the optimal storage location. And a heuristic rule is suggested in order to overcome computational difficulties of the optimization model. The performance of the rule is evaluated by comparing it with the result of DP model.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.501-508
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• 2000

It is demanded to find the dynamic model of a real structure to design a controller. However, as the structure has inherently infinite number of degree-of-freedom, it is impossible to obtain an exact dynamic model of the structure. Instead a reduction model with finite degree-of-freedom is used for the design of a controller. So there exists uncertainty between a real model and a reduction model which causes poor performance of control. All these uncertainties can degrade the control performance and even cause the control instability. Thus, robust control strategy considering the above uncertainties can be an alternative one to guarantee the performance and stability of the control. This study deals with the experimental verification of robust controller design for the active mass driver. $\mu$-synthesis technique is employed as a robust control strategy. Some weights are chosen based on the difference between the initial plant with which the controller is designed and the perturbed plant to be controlled having the actuator uncertainty. The robustness of $\mu$-synthesis technique is compared with the result of LQG strategy, which does not consider the uncertainty.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.262-269
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• 2005

Optimization of a satellite structure under severe space launching environments is performed considering various design constraints. Simulate annealing, one of combinatorial optimization techniques, is used to optimize the satellite. The optimization results by the simulated annealing are compared to those by the method of modified feasible direction and genetic algorithm. Ten bar truss structure is optimized for feasibility study of the simulated annealing. Finally, the satellite structure is optimized by the simulated annealing algorithm under space environment. Weights of the satellite upper platform and propulsion module are minimized with consideration of several static and dynamic constraints. MSC/NASTRAN is used to find the static and dynamic responses. Simulated annealing has been programmed and integrated with the finite element analysis program for optimization. It is shown that the simulated annealing algorithm can be extended to the optimization of space structures.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.4
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• pp.75-81
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• 1997

This study was performed to evaluate the engineering properties of surlightweight polymer concrete using synthetic lightweight aggregate. The following conclusions were drawn; 1. The unit weight was in the range of 0.849~0.969t/$m^3$, the unit weights of those concrete were decreased by 58 ~ 63% than that of the normal cement concrete. 2. The highest strength was achieved by $P_1$, and compressive strength was increased by 93% and bending strength by 364% than that of the normal cement concrete, respectively. 3. The ultrasonic pulse velocity was in the range of 2, 346~2, 702m/s, which was low compared to that of the normal cement concrete. 4. The dynamic modulus of elasticity was in the range of $1.561{\times} 10{^5}~1.916{\times} 10{^5}kgf/cm^2$, which was approximately 52~98% of that of the normal cement concrete. 5. The compressive and bending strength were increased with the increase of unit weight. But, the dynamic modulus of elasticity and ultrasonic pulse velocity were decreased with the increase of unit weight.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1848-1849
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• 2006

The new robust controller design method is proposed for the flight control systems with model uncertainties. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the "explosion of complexity" problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.133-139
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• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.12
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• pp.518-525
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• 2006

This paper presents the adaptive robust control method for the flight control systems with model uncertainties. The proposed control system can be composed simply by a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the 'explosion of complexity' problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems, and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• Journal of Industrial Technology
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• v.19
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• pp.15-23
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• 1999

Active damping system is generally used for the vibration suppression and precise motion control for the flexible structure. This application can be easily found on the space structure and driving mechanism of optical storage devices. Although a control system using the flexible structure has many advantages over using rigid mechanism in driving energy saving, system weights, and etc., more complex and precise control strategies are required. A position control system using flexible structure and the concept of active damper is designed and manufactured, which is driven by slide DC motor and moving coil motor located at the tip of the flexible beam. Dynamic characteristics of this system are investigated by analytic and experimental ways. By the comparison of those two results, a nominal reference model for this system is proposed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11b
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• pp.46-51
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• 2001

For Fast Real-time Recognition of Nonlinear Error Data, a new Neural Network algorithm which recognized the map in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion, In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of map, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear map information is processed,