• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.516-518
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• 1999

This paper presents an feed-forward neural network design instead PI controller for the speed control of an Induction Motor. The design employs the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE). Emulator identifies the motor by simulating the input and output map. In order to update the weights of the Controller. Emulator supplies the error path to the output stage of the controller using backpropagation algorithm. and then Controller produces an adequate output to the system due to neural networks learning capability. Therefore it becomes adjustable to the system with changing characteristics caused by a load. The speed control based on neural networks for induction motor is implemented by a vector controlled induction motor. The simulation results demonstrate that actual motor speed with neural network system well follows the reference speed minimizing the error and is available to implement on the vector control theory.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.950-960
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• 2010

In this paper, a multi-input multi-output(MIMO) integral variable structure system with an integral-augmented sliding surface is designed for the improved robust control of uncertain multivariable system under the matched persistent disturbance. To effectively remove the reaching phase problems, the integral augmented sliding surface is proposed. Then for its design, the eigenstructure assignment technique is introduced to. To guarantee the designed performance against the persistent disturbance, the stabilizing control for multi-input system is also designed to generate the sliding mode on the integral sliding surface. The stability of the global system together with the existence condition of the sliding mode are investigated and proved for the case of multi input system in the presence of uncertainty and disturbance. The reaching phase is completely removed in proposed MIMO VSS by satisfying the two requirements. An example and computer simulations will be present for showing the usefulness of algorithm.

• Journal of Power Electronics
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• v.2 no.2
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• pp.130-138
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• 2002

The windup phenomenon appears and degrades control performance when a controller with integrating action is used and plant input is limited. An anti-windup integal-proportional(IP) controller is proposed for the variable-speed moter drives and it is experimentally applied to the speed control of a vector-controlled induction moter driven by a pulse width modulated (PWM) voltage source inverter (VSI). The consistency range of the IP controller is firstly derived and the intergal state is controlled to salisfy always the consistency range according to whether the the controller output is saturated or not. Although the operating condition like moter load or speed command is changed under the limited plant input, It is expermentally verified that the speed response has much improved performance, such as no overshoot and fast settling time, and the maximmum plant input is also effectively utilized.

• Proceedings of the KIPE Conference
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• 2010.11a
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• pp.363-364
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• 2010

Most LED drivers uses current control method to adjust LED current. Using AC power grid such as off-line converter, Buck topology is popular because input voltage of LED driver is much higher than LED output voltage. Normally DCM current control is more popular than CCM current mode control in the range of below 50W, But DCM characteristics are dependent on the input voltage variation. This paper deals with what should be considered in DCM for LED driver with valley fill circuit.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.435-441
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• 2003

This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.87-90
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• 2003

There are generally two temperature control methods using ac voltage regulators - the phase control method and the on-off control method. The phase control method uses thyristor's angles of extinction for the output power regulation and the on-off control method uses the control of on and off times for the output power regulation. Both of methods have the problems that are the unbalance of the three phase and surge current caused system's destruction. The main object of this study proposes a solution of problems of surge currents and unbalance of three phase when thyristor voltage regulator is switching. To solve the problem, It is proposes that the solution of surge currents is using a tap transformer and an additional switch with adjustable resistance and applies zero crossing of switching voltage of two input line to power load. This method is discuss and verified by computer simulations and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.344-349
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• 1990

Under the assumption that process input/output data are sufficiently rich to allow reasonable plant identification, a long-range predictive control method for SISO bilinear plant is derived. In order to ensure offset-free behaviour of the control method, a new bilinear CARIMA model with variable dead-time is introduced. Furthermore, to extend the maximum output prediction horizon, the future predicted outputs in the bilinear term are assumed to be equal to the known future set-points. With a classical recursive adaptation algorithm, the proposed control scheme is capable of stable control of bilinear plants with variable parameters, with variable dead-time, and with a model order which changes instantaneously. Several simulation results demonstrate the characteristics of the proposed bilinear model predictive control method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.81-86
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• 1997

It is proposed, for fuzzy combustion control system of refuse incinerator to find the relationship between inputs and outputs and to generate rules to control by using rough set theory. It is not easy to find out the corresponding inputs for each output and the control rules with incomplete or imprecise information consisting expert knowledge, process and manipulator values in the field, and operation manual for the given system. Most decision problems can be formulated employing decision table formalism. A decision table on fuzzy combustion control system for refuse incinerator is simplified and produces control(rules). The I/O realtions and the control rules found by rough set theory are compared with the previous result.