• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.7-12
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• 1990

In this paper, we propose a new manipulator control scheme based on the CMAG neural network. The proposed control consists of two components. The feedforward component is an output of trained CMAC neural network and the feedback component is a modified sliding mode control. The CMAC accepts the position, velocity and acceleration of manipulator as input and outputs two values for the controller : One is the nominal torque used for feedforward compensation(M1 network) and the other is the inertia matrix related information used for the feedback component(M2 network). Since the used control algorithm guarantees the robust trajectory tracking in spite of modeling errors, the CMAC mapping errors due to the memory limitation are little worth consideration.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.414-420
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• 1997

An intelligent speed control system for marine diesel engines is presented. The approach adopt¬ed is to use a conventional PID controller for normal operation and a feedforward controller for adaptive control. The feedforward controller is a neural network. The neural network is the inverse dynamics model of the plant, which is being trained on line. The parametric model of the diesel engine is represented in a linear second-order system, with a first-order combustion part and a revolution part each at a normal operating point. The time delay in the control of the com¬bustion part is approximated to the first-order system. The tuned PID parameters are set based on the model for normal operating point. To obtain the inverse dynamics of the diesel engine system, two neural networks are used, one for inverse, the other for forward dynamics. The former is posi¬tioned across the plant to learn its inverse dynamics during operation, and the latter is placed in series with the controlled plant. Simulation results are presented to illustrate the applicability of the proposed scheme to intelligent adaptive control of diesel engines.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1186-1193
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• 1991

This paper proposed a trajectory controlmethod for a robot manipulator by using neural networks. The total torque for a manipulator is a sum of the linear feedback controller torque and the neural network feedfoward controller torque. The proposed neural network is a multilayer neural network with time delay elements, and learns the inverse dynamics of manipulator by means of PD(propotional denvative)controller error torque. The error backpropagation (BP) learning neural network controller does not directly require manipulator dynamics information. Instead, it learns the information by training and stores the information and connection weights. The control effects of the proposed system are verified by computer simulation.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2714-2716
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• 2005

In this paper, we designed the adaptive fuzzy logic controller for crane system using neural network and real-coding genetic algorithm. The proposed algorithm show a good performance on convergence velocity and diversity of population among evolutionary computations. The weights of neural network is adaptively changed to tune the input/output gain of fuzzy logic controller. And the genetic algorithm was used to leam the feedforward neural network. As a result of computer simulation, the proposed adaptive fuzzy logic controller is superior to conventional controllers in moving and modifying the destination point.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.367-375
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over modeling error, parameter variation and disturbance. Second, it needs accurate performance which have small overshoot phenomenon and steady state error to avoid colliding with ground surface or obstacles. Third, it needs continuous control input to reduce the acoustic noise and propulsion energy consumption. Finally, it needs interpolation method which can sole the speed dependency problem of controller parameters. To solve these problems, we propose a depth control method using Fuzzy Sliding Mode Controller with feedforward control-plane bias term and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.8
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• pp.557-564
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system can be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. In addition, the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Signal Processor DS1102 Board (TMS320C31).

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.150-155
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• 1993

The neural network MRAC system is presented. The purpose of this paper is applied to a plant that is to be controlled in a strongly nonlinear environment. The proposed system has a learning and adaptive ability in the varying environment by using the back-propagation learning algorithm based on Lyapunov stability theory. N.N. regulator is a part of overall system and is guaranteed to be stable in initial stage. Nonlinear terms of the varying mass, colilori, centifugal, and gravity are compensated for by feedforward N.N. regulator. And the feedback controller (adaptive mechanism) works to eliminate errors of position, velocity which the feedforward controller cannot compensate for. Finally, the proposed system will be demonstrated by simulation of a two d.o.f robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.147-150
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• 1996

This paper describes controller design and implementation method for controlling the temperature distribution in a thermal stratified wind tunnel(TSWT) by using a neural network algorithm. It is impossible to derive a mathematical model of the relation between heat inputs and temperature outputs in the test section of the TSWT governed by a nonlinear turbulent flow. Thus inverse neural network models with a multi layer perceptron structure are used in a feedforward control loop and feedback control loop to generate an arbitrary temperature distribution in the test section of the TSWT.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.33-43
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• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.359-361
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• 1994

This paper propose a Neural - GA-ES DC motor speed controller. The purpose is to achieve accurate trajectory control of the motor speed. A feedforward neural network structure is used for the controller. Genetic algorithm and evolution strategy is used for learning controller. Simulations are performed to demonstrate the effectiveness of proposed genetic algorithm and evolution strategy with neural structure.