• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.55-63
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• 2004

This paper presents neural network based controller using the feedback error loaming technique for a heavy load pointing system. Also the mathematical model was developed to analyze heavy load pointing system. The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. The proposed controller is compared with the conventional PI controller through simulations, and the results show that the pointing accuracy of the proposed control system are improved against the disturbance induced by vehicle running on the bump course.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.152-156
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• 2008

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.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.157-165
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• 1995

In this paper the comparison between the neural networks and traditional approaches as nonlinear system identification methods are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. Computer simulation for an analytic dynamic model of a single input single output nonlinear system has been done for all the chosen models. Model validation for the obtained models also has been done with testing inputs of the sinusoidal, ramp and the noise ramp.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.8
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• pp.19-26
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• 1995

In this paper, a fuzzy-neural hybrid control approach is proposed for controlling a mobile robot that can avoid an unexpected obstacle in a navigational space. First, to describe the global structure of a known environment, a heuristic collision-free space band is introduced. Based on the band, the moving information in the known environment is trained to a neural controller. Then, during the execution of a mobile robot navigation moving information at each position is given the neural controller. If the mobile robot encounters an unexpected obstacle, a fuzzy controller activates to avoid the unexpected obstacle. Finally, some numerical examples are presented to demonstrate the control algorithm.

• Journal of Korean Port Research
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• v.6 no.2
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• pp.3-24
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• 1992

In the fuzzy control of shop the qualitative knowledge and information that the ship's operators have acquired through their experience can be logically described by the Linguistic control Rule (LCR). The algorithm of the control is made of the LCR and the control of the shop is performed by processing this algorithm implementing a computer. The problem in the fuzzy control is that it is very difficult to describe qualitative human knowledge in the LCR correctly. To tackle this difficulty a Fuzzy Neural Network (FNN) was introduced in this paper. The characteristics of the multi-layer FNN control system applied to the ship's steering system is investigated through the computer simulation, and the results were compared with those of the ordinary fuzzy control system of a ship. The results showed that the FNN method is a very effective to translate human knowledge into the LCR.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.641-647
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• 2009

In this paper, a new filtering scheme for adaptive cruise control (ACC) system is presented. In the proposed scheme, the identification of the mode of the preceding vehicle is considered as a classification problem and it is done by a neural network classifier. The neural network classifier outputs a posterior probability of the mode of the preceding vehicle and the probability is directly used in the IMM framework. Finally, ten scenarios are made and the proposed NIMM is tested on them to show its validity.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.4
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• pp.33-42
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• 2010

The induction motor drive applied to field oriented control is widely used in industry applications. However, it is deceased performance and authenticity by saturation, temperature changing, disturbance and parameters changing because modeling of induction motor is nonlinear and complex. In order to control variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper proposes self tuning PI controller based on fuzzy-neural network(FNN)-PI controller that is implemented using fuzzy control, neural network, and adaptive fuzzy controller(AFC). Also, this paper proposes estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FNN-PI, AFC and ANN controller. Also, this paper proposes the anlysis results to verify the effectiveness of controller.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1497-1508
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• 1993

Real-time mobile robot controllers usually have been designed focused on control theory without paying attention to the importance of system integration. This paper demonstrates that autonomous mobile robots require a real-time controller with a wide range of capabilities in addition to control theory. An architectural frame work supporting these capabilities has been designed in actual hardware environments. Individual modules such as a path planner, a path tracking controller, position estimators, wheel controllers and other cruical elements have been successfully integrated into the control system using this frame work. The overall performance of the system was investigated via a series of tracking experiments with a prototype mobile robot named LCAR deveoped in the laboratory. The context of the research involves the architecture, its implementation and experimental results.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.29-37
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• 2006

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network 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. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.