• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.400-410
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• 1992

This paper deals with a design of neural network control architectures for robot motion. Three types of control architectures are designed as follows : 1) a neural network control architecture which has the same characteristics as computed torque method 2) a neural network control architecture for compensating the control error on computed torque method with fixed feedback gain 3) neural network adaptive control architecture. Computer simulation of PUMA manipulator with 6 links is conducted for robot motion in order to examine the proposed neural network control architectures.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.79-89
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• 1993

This paper deals with a computer aided control of an adjustment process for the complete electronic devices by means of an application of artificial neural network and an implementation of neuro-controller for intelligent control. Multi-layer neural network model is employed as artificial neural network with the learning method of the error back propagation. Information initially available from real plant under control are the initial values of plant output, and the augmented plant input and its corresponding plant output at that time. For the intelligent control of adjustment process utilizing artificial neural network, the neural network emulator (NNE) and the neural network controller(NNC) are developed. The initial weights of each neural network are determined through off line learning for the given product and it is also employed to cope with environments of the another product by on line learning. Computer simulation, as well as the application to the real situation of proposed intelligent control system is investigated.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1557-1560
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• 1997

In this paper, we have built an experimental magnetic levitation system for a possible use of control education. We have give a mathermatical model of the nonlinear system and have shown the stability region of the linearized system when it is controlled by a PD controller. We also proposed a neural network control system which uses a neural network as a feedforward controller thgether with a conventional feedback PF controller. We have generated a desired output trajectory, which was designed for the benefit of the generalization of the neural network controller, and trained the desired output trajectory, which was desigend for the benefit of the generalization of the neural netowrk controller, and trained a neural network controller with the data of the actual input and the output of the system obtained by applying the desired output trajectroy. A good tracking performance was observed for both the desired trajectiories used and not used for the neural network training.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.196-200
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.136-139
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.