• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.618-624
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• 2005

We present a new state-space approach to construct a dynamic output feedback controller which stabilizes a class of linear time invariant systems. All the states of the given system are not measurable and only the output is used to design the stabilizing control law. In the design scheme, however, we first assume that the given system can be stabilized by a feedback law composed of the output and its derivatives of a certain order. Beginning with this assumption, we systematically construct a dynamic system which removes the need of the derivatives. The main advantage of the proposed controller is regarding the controller order, which may be smaller than that of conventional output feedback controller. Using a simple numerical example, it is shown that the order of the proposed controller is indeed smaller than that of reduced-order observer based output feedback controller.

• Journal of Power Electronics
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• v.7 no.1
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• pp.21-27
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• 2007

This paper investigates a PID fuzzy controller for output voltage regulation of a current-doubler-rectified asymmetric half-bridge (CDRAHB) DC/DC converter. The controller is a PD-type fuzzy controller in parallel with a linear integral controller. The PD type fuzzy controller is for providing the varying gain at the different operating conditions to regulate the output voltage. The linear integral controller is for removing the steady-state error of the output voltage. In order to show the outstanding dynamic characteristics of the proposed controller, PSIM simulation studies are carried out and compared to the results for which the conventional loop gain design method is used.

• Journal of IKEEE
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• v.11 no.4
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• pp.213-218
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• 2007

We consider a web transport system. The objective of this paper is to design the output feedback controller such that the controller can track a desired tension and processing speed on web transport system. We propose the new design method using observer and feedback linearization technique. The proposed method use a nonlinear feedback to transform to linear system and high gain observer to estimate the state value. We show that the proposed controller can achieve the control object using only output. We show a performance of controller via the simulation.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.43-46
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• 2001

We, in this paper, design the Sugeno-models fuzzy controller by using the membership function modification algorithm and ANFIS, which are clustering and learning the input-output data. The membership function modification algorithm constructs the more concrete fuzzy controller by clustering the input-output data from the fuzzy inference system. ANFIS construct the Sugeno-models fuzzy controller by learning the input-output data from the above controller. We showed that the fuzzy controller designed by our method could have the stable learning and the enhanced performance.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.291-294
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• 2006

This paper presents a decentralized input-output feedback linearizing controller for a multi-machine power system. Firstly, the controller is designed using input-output feedback linearization for modified outputs. Then we present a guideline for selecting gains of the controller and parameters in the modified outputs. Simulations illustrate the effectiveness of the proposed control scheme and the selection guideline.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.47-49
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• 2004

In this correspondence, an new approach to design a fuzzy controller for system with uncertain output backlash to have good tracking performance is presented. Without using a compensation mechanism or backlash inverse, the fuzzy control mechanism is designed to implicitly compensate the delay effect arising from an uncertain output backlash and to make the output backlash stable without limit cycles. The proposed method designs a model-based fuzzy controller for a one-input one-output linear plant with output backlash. The effectiveness of the designed fuzzy controller is illustrated by the simulation.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.4
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• pp.345-352
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• 2012

This paper proposes a new method to reduce 120Hz output voltage ripple of LLC converter using resonant voltage controller. This method can reduce the 120Hz output voltage ripple with very high gain at this frequency by the resonant controller with previous PI voltage controller. The reason why the voltage ripple can be reduced is explained by the Bode diagram comparing with the previous PI controller. The simulation with Matlab/Simulink is carried out for this resonant controller and the simulation results show that resonant controller can reduce the 120Hz output voltage ripple. Experiments with DSP controller also carried out and the experimental results also show that the usefulness of the proposed voltage controller.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.10
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• pp.1031-1036
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• 2015

Proportional-integral-derivative (PID) controllers are widely used in industrial sites. Most tuning methods for PID controllers use an empirical and experimental approach; thus, the experience and intuition of a designer greatly affect the tuning of the controller. The representative methods include the closed-loop tuning method of Ziegler-Nichols (Z-N), the C-C tuning method, and the Internal Model Control tuning method. There has been considerable research on the tuning of PID controllers for single-input single-output systems but very little for multi-input multi-output systems. It is more difficult to design PID controllers for multi-input multi-output systems than for single-input single-output systems because there are interactive control loops that affect each other. This paper presents a tuning method for the PID controller for a two-input two-output system. The proposed method uses a real-coded genetic algorithm (RCGA) as an optimization tool, which optimizes the PID controller parameters for minimizing the given objective function. Three types of objective functions are selected for the RCGA, and each PID controller parameter is determined accordingly. The performance of the proposed method is compared with that of the Z-N method, and the validity of the proposed method is examined.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.798-800
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• 1995

The neural network predictive controller(NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output(Neural Network Predictor) and the other one is for control the plant(Neural Network Controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and prediction error. The NNP forecasts the future output based upon the current control input and the estimated control output. The method is applied to the control of temperature in boiler systems. The proposed NNPC is compared with the other conventional control methods such as PID controller, neural network controller with specialized learning architecture, and one-step-ahead controller. The computer simulation and experimental results show that the proposed method has better performances than the other methods.