• Journal of Korean Association for Spatial Structures
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• v.15 no.2
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• pp.105-112
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• 2015

A shared tuned mass damper (STMD) was proposed in previous research for reduction of dynamic responses of the adjacent buildings subjected to earthquake loads. A single STMD can provide similar control performance in comparison with two traditional TMDs. In previous research, a passive damper was used to connect the STMD with adjacent buildings. In this study, a smart magnetorheological (MR) damper was used instead of a passive damper to compose an adaptive smart STMD (ASTMD). Control performance of the ASTMD was investigated by numerical analyses. For this purpose, two 8-story buildings were used as example structures. Multi-input multi-output (MIMO) fuzzy logic controller (FLC) was used to control the command voltages sent to two MR dampers. The MIMO FLC was optimized by a multi-objective genetic algorithm. Numerical analyses showed that the ASTMD can effectively control dynamic responses of adjacent buildings subjected to earthquake excitations in comparison with a passive STMD.

• Food Science and Preservation
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• v.9 no.2
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• pp.137-143
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• 2002

Temperature and humidity are the most important factors and should be effectively controlled for the cold storage of graius. Fuzzy logic can be easily implemented to the MIMO(Multi-Input Multi-Output) control systems. For the cold storage in grain bin, fuzzy logic was applied to an air conditioning system. The capacities of the grain bin and the air conditioner are 80 tons and 30㎾, respectively. Also, the target values of temperature and relative humidity in outlet duct of the air conditioner were 8$\^{C}$ and 75%, respectively. In order to control temperature and relative humidity of air, a damper in inlet duct was manipulated for temperature control and a heater was used for humidity control. Temperature deviation and change of temperature deviation were used as input parameters for the fuzzy system. Humidity was only considered as a load. The experimental results showed that the controlled temperature of exhausted air was maintained at 8$\pm$2$\^{C}$. Relative humidity of the air was also controlled at the target relative humidity of 50∼80%.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.407-409
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• 1998

In order to improve the tracking performance of $2{\times}2$ multivariable control systems, a fuzzy control algorithm with feedforward compensator is represented. The method consists in two steps. First, neglecting interconnections. one designs a fuzzy controller to each individual loop. In the second stage, low-order transfer functions of outputs to reference inputs are estimated. We propose a design method of the feed forward compensator based on the transfer functions. An illustrative example are shown.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.11
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• pp.514-519
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• 2001

This Paper presents an application of a on-line self-organizing fuzzy logic controller to a boiler system of fossil-power plant. A boiler-turbine system is described as a MIMO nonlinear system in this paper. Then, three single loop fuzzy logic controllers are designed independently. The control rules and the membership functions of proposed fuzzy logic control system are generated automatically without using plant model. The simulation shows successful results for wide range operation of boiler system of fossil-power plant.

• ICROS
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• v.1 no.1
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• pp.16-16
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• 1995

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.16-20
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• 1999

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.194-198
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• 1998

A novel fuzzy basis function vector- based adaptive control approach for Multi-input and Multi-output(MIMO) system is presented in this paper, in which the nonlinear plants is first linearised, the fuzzy basis function vector is then introduced to adaptively learn the upper bound of the system uncertainty vector, and its output is used as the paramenters of the compensator in the sense that both the robustness and the asymptotic error convergence can be obtained for the closed loop nonlinear control system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.171-180
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• 2014

In this paper, a multiple sliding surface (MSS) controller for a twin rotor multi-input-multioutput system (TRMS) with mismatched model uncertainties is proposed. The nonlinear terms in the model are regarded as model uncertainties, which do not satisfy the standard matching condition, and an MSS control technique is adopted to overcome them. In order to control the position of the TRMS, the system dynamics are pseudo-decomposed into horizontal and vertical subsystems, and two MSSs are separately designed for each subsystem. The stability of the TRMS with the proposed controller is guaranteed by the Lyapunov stability theory. Some simulation results are given to verify the proposed scheme, and the real time performances of the TRMS with the MSS controller show the effectiveness of the proposed controller.