• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.308-317
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• 2006

To control the tip position of a flexible-link manipulator, a neural network (NN) controller is proposed in this paper. The dynamics error used to construct NN controller is derived based on output redefinition approach. Without the filtered tracking error, the proposed NN controller can still guarantee the closed-loop system uniformly asymptotically stable as well as NN weights bounded. Furthermore, the tracking error of desired trajectory can converge to zero with the proposed controller. For comparison an NN controller with filtered tracking error is also designed for the flexible-link manipulator. Finally, simulation studies are carried out to verify the theoretic results.

• Journal of Power Electronics
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• v.19 no.4
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• pp.835-845
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• 2019

This paper presents a robust-optimal control strategy to improve the output-voltage error-tracking and control capability of a DC-DC boost converter. The proposed strategy employs an optimized Fractional-order Proportional-Integral (FoPI) controller that serves to eliminate oscillations, overshoots, undershoots and steady-state fluctuations. In order to significantly improve the error convergence-rate during a transient response, the FoPI controller is augmented with a pre-stage nonlinear error-modulator. The modulator combines the variations in the error and error-derivative via the signed-distance method. Then it feeds the aggregated-signal to a smooth sigmoidal control surface constituting an optimized hyperbolic secant function. The error-derivative is evaluated by measuring the output-capacitor current in order to compensate the hysteresis effect rendered by the parasitic impedances. The resulting modulated-signal is fed to the FoPI controller. The fixed controller parameters are meta-heuristically selected via a Particle-Swarm-Optimization (PSO) algorithm. The proposed control scheme exhibits rapid transits with improved damping in its response which aids in efficiently rejecting external disturbances such as load-transients and input-fluctuations. The superior robustness and time-optimality of the proposed control strategy is validated via experimental results.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.244-246
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• 2001

In a full digital implementation of a current regulator, the voltage output is inevitably delayed due to arithmetic calculation and PWM. In case of the synchronous frame current regulator, the time delay is accompanied by the rotation of frame. In some applications in which the ratio of sampling frequency to output frequency is not high enough, such as high power drive or super high-speed drive, it is known that the effect of rotation of frame during the delay time causes phase and magnitude error in the voltage output. The error degrades the dynamic performance and can bring about the instability of current regulator at high speed. It is also intuitively known that advancing the phase of voltage output can mitigate the instability. In this paper, the instability problems are studied analytically and a compensation method for the error has been proposed. By means of computer simulation and complex root locus analysis, comparative study with conventional methods is carried out and the effectiveness of proposed method is verified.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.1-5
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• 1996

In the unification method of classical linear control and variable structure control, a problem that the error between the actual plant output and the nominal plant output exists consistently is solved by replacing the nominal plant with an adaptive observer. Since the exponentially convergent adaptive observer is used, the adaptive observer output converges to the actual plant output rapidly. So, the error does not exist after all and as a result the performance degradation of the control system is prevented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1596-1603
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• 1996

A single body type of fiber-optic current and voltae sensor using a rare earth doped YIG and a bismuth silicon oxide single crystsl is proposed, which is used for simultaneous measurement of the AC electric current and AC electric voltage over the trasmission lines. Experimental results showed that the fiber-optic current sensor has the maximum 7.5% error within the current range of 0A to 400A, and the fiber-optic voltage sensor has the maximum 0.87% error within the current range of 0V to 400V. The output waveforms of proposed fiber-optic sensor system has a good agreement with output waveforms of conductor current and voltage. Experimental results proved that the output of fiber-optic current sensor is not affected by the electric voltage applied to the fiber-optic voltage sensor, and also, that the output of fiber-optic voltage sensor is not affected by the electric current applied to the fiber-optic current sensor.

• 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.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1943-1945
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• 2001

In this paper, we present an adaptive output feedback control scheme for a class of uncertain nonlinear output-feedback form with time-varying parameters to which adaptive observer backstepping technique may not be applicable directly. In observer design, with the introduction of design function, we can deal with time-varying parameters in a very effective way. By the presented scheme, estimation error can be tuned to a desired small region around the origin via the design constants. Consequently, the observer with the presented design functions and the backstepping methodology achieve a robust regulation of the output tracking error while maintaining boundedness of all the signals and states.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.1
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• pp.89-96
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• 2022

In this paper, to evaluate the output quality of the cementitious composite mixture for printing with the ME method for construction 3D printer, visual inspection of the output appearance and the dimensional error rate, compressive strength and flexural strength of the output were measured. As a result of the test, the mixing design with excellent output appearance was P1-2, P1-4, P2-5, P2-6, and the mixing design with good output appearance was P0-1, P1-1, P1-3, P1-6, P1-7 and P2-4. Of these mixing designs, P0-1 and P2-6 had the lowest dimensional error rates As a result of evaluating the compressive strength and flexural strength of the various mixing designs, the Mixing design with excellent output designs showed good mechnical properties. However, mixing designs with excellent mechanical properties does not necessarily have excellent output quality. Therefore, in order to accurately evaluate the output quality, it is judged that visual inspection and dimensional error rate inspection should be performed first, and then the mechanical characteristics should be reviewed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.504-506
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• 1999

In this paper, we propose a method which control parametric uncertainty systems using PID controller by fuzzy auto tuning. We get the error and the error change rate of plant output correspond to the initial value of parameter using the Ziegler-Nickols tuning and determine the new proportional gain$(K_p)$ and the integral time $(T_i)$ from fuzzy tuner by the error and error change rate of plant output as a membership function of fuzzy theory. The Fuzzy Auto-tuning algorithm for PID controller operate to adapt variable parameter of plant in parametric uncertainty systems. It is shown this method considerably improve the transient response at computer simulation.