• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.573-591
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• 2004

A hybrid control system is presented for seismic-resistant building structures with and without soil-structure interaction (SSI). The hybrid control is a damper-actuator-bracing control system composed of passive and active controllers. An intelligent algorithm is developed for the hybrid system, in which the passive damper is designed for minor and moderate earthquakes and the active control is designed to activate when the structural response is greater than a given threshold quantity. Thus, the external energy for active controller can be optimally utilized. In the control of a multistory building, the controller placement is determined by evaluating the optimal location index (OLI) calculated from six earthquake sources. In the study, the soil-structure interaction is considered both in frequency domain and time domain analyses. It is found that the interaction can significantly affect the control effectiveness. In the hybrid control algorithm with intelligent strategy, the working stages of passive and active controllers can be different for a building with and without considering SSI. Thus SSI is essential to be included in predicting the response history of a controlled structure.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.5
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• pp.27-34
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• 2005

To accomplish various and complex tasks by intelligent robots, improvement is needed not only in mechanical system architecture but also in control system architecture. Hybrid control architecture has been suggested as a mutually complementing architecture of the weak points of a deliberative and a reactive control. This paper addresses a control architecture of robots, and a behavior representation methodology. The suggested control architecture consists of three layers of deliberative, sequencing, and reactive as hybrid control architecture. Multi-layer behavior model is employed to represent desired tasks. 3D simulation will be conducted to verify the applicability of suggested control architecture and behavior representation method.

• Proceedings of the KIEE Conference
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• summer
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• pp.1245-1247
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• 2004

This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using hybrid intelligent control. The hybrid combination of neural network and adaptive fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using adaptive neural network fuzzy(A-NNF) and estimation of speed using artificial neural network(ANN) controller. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.984-988
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• 2005

To accomplish various and complex tasks by intelligent robots, improvement is needed not only in mechanical system architecture but also in control system architecture. Hybrid control architecture has been suggested as a mutually complementing architecture of the weak points of a deliberative and a reactive control. This paper addresses a control architecture of robots, and a behavior representation methodology. The suggested control architecture consists of three layers of deliberative, sequencing, and reactive as hybrid control architecture. Multi-layer behavior model is employed to represent desired tasks. 3D simulation will be conducted to verify the applicability of suggested control architecture and behavior representation method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1086-1091
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• 2005

This paper focuses on intelligent control of induction motor by hybrid system consisting of GA-PSO. Induction motor has been using in industrial area. However, it is challengeable on how we control effectively. From this point, an optimal solution using GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is introduced to intelligent control. In this case, it is possible to obtain local solution because chromosomes or individuals which have only a close affinity can convergent. To improve an optimal learning solution of control, This paper deal with applying PSO and Euclidian data distance to mutation procedure on GA's differentiation. Through this approaches, we can have global and local optimal solution together, and the faster and the exact optimal solution without any local solution. Four test functions are used for proof of this suggested algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.896-902
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• 2004

The purpose of the study of multi-robot system is to realize multi-robot system easy for the control of robot system in case robot is adapted in the complicated environment of task structure. The purpose of the study of multi-robot system is to realize multi-robot system easy for the control of robot system in case robot is adapted in the complicated environment of task structure. To make real time control possible by making effective use of recognized information in this dynamic environment, suitable distribution of tasks should be made in consideration of function and role of each performing robots. In this paper, IHMA (Intelligent Hybrid Modular Architecture) of Intelligent combined control architecture which utilizes the merits of deliberative and reactive controllers will be suggested and its efficiency will be evaluated through the adaptation of control architecture to representative multi-robot system.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.980-984
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• 2001

When robot manipulatros as mathematically modeled. uncetainties may not be avoided. The uncertain factors come from imperfect knowledge of system parameters, payload change. friction, external disturbance and etc. In this work, we proposed a class of robust hybrid control of manipulatosrs. We propose a class of expanded robust hybrid control with the separated bound function and the simulation results are provided to show the effectiveness of the algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1180-1188
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• 2008

As many systems depend on electronics, concern for fault tolerance is growing rapidly in the safety critical system such as intelligent vehicle. In order to make system fault tolerant, there has been a body of research mainly from aerospace field including predictive hybrid redundancy by Lee. Although the predictive hybrid redundancy has the fault tolerant mechanism to satisfy the fault tolerant requirement of safety crucial system such as x-by-wire system, it suffers form the variability of prediction performance according to the input feature of system. As an alternative to the prediction method of predictive hybrid redundancy for robust fault tolerant, Kalman prediction has attracted some attention because of its well-known and often-used with its structure called Kalman hybrid redundancy. In addition, several numerical simulation results are given where the Kalman hybrid redundancy outperforms with predictive smoothing voter.