• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.446-449
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• 1995

In this paper, the accurate end position control method of ultraprecision machine tool post using piezoelectric material as an micro positonong devics is presented. This method employs the classical PID feedback and uses an additional notch filter which eliminates the resonance characteristics of controlled plant. And the simple predictor is added to make use of the future value of desired input for better tracking performance. To show the feasibilty of proposed method, the PC-based experimental apparacy can be obtained. Using method, Al specimen of diameter 100mm was cut under practical machining condition to test the practicability of proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1041-1044
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• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1671-1676
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• 1991

Direct digital design of computed torque controllers for a robot manipulator is discussed in this paper. A simple discrete-time model of the robot manipulator obtained by Euler's method is used for the design. Taking account of computation delay in the digital processor, we propose predictor-based designs of the PD and PID type controllers. The PID type controller is designed based on a modified version of the discrete-time integral controller proposed by Mita. For both controllers, the same formulas can be used to determine the feedback gains. A simulation example is presented to compare the robustness of the proposed controllers against physical parameter variations.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.323-327
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• 1995

A new air knife system for coating thickness control in hot dip galvanizing process had been developed and installed on the CGL in Pohang Steel Works, POSCO. This new system consists of air knives with remotely adjustable nozzle slot and an automatic control system which can control both longitudinal and traverse coating deviations. Based on the optimal control algorithm, a traverse coating deviation control was designed. The controller controls the lip profile of the air knives with flexible structure according to the deviation of coating weight. From the measured values which are dependent on the strip width, the lip gaps are calculated with optimal algorithm and the model of the coating deviation. Time delay between knives and a coating thickness gauge is solved by the Smith Predictor.

• 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.

• Journal of radiological science and technology
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• v.45 no.5
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• pp.439-448
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• 2022

The purpose of this study was to analyze the results from statistical process control (SPC) to recommend upper and lower control limits for planning parameters based on delivery quality assurance (DQA) results and establish our institutional guidelines regarding planning parameters for helical tomotherapy (HT). A total of 53 brain, 41 head and neck (H & N), and 51 pelvis cases who had passing or failing DQA measurements were selected. The absolute point dose difference (DD) and the global gamma passing rate (GPR) for all patients were analyzed. Control charts were used to evaluate upper and lower control limits (UCL and LCL) for all assessed treatment planning parameters. Treatment planning parameters were analyzed to provide its range for DQA pass cases. We confirmed that the probability of DQA failure was higher when the proportion of leaf open time (LOT) below 100 ms was greater than 30%. LOT and gantry period (GP) were significant predictor for DQA failure using the SPC method. We investigated the availability of the SPC statistic method to establish the local planning guideline based on DQA results for HT system. The guideline of each planning parameter in HT may assist in the prediction of DQA failure using the SPC statistic method in the future.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.4-172
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• 2001

In this paper, the Generalized Predictive Control(GPC) method based on Fuzzy Neural Networks(FNNs) is presented for the control of chaotic nonlinear systems without precise mathematical models. In our method, FNNs is used as the predictor whose parameters are tuned by the error between the actual output of nonlinear chaotic system and that of FNNs model. The parameters of GPC controller are adjusted via the gradient descent method where the difference between the actual output and the reference signal is used as a control error. Finally, computer simulation on the representative continuous-time chaotic system(Duffing system) is presented to demonstrate the effectiveness of our chaos control method.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.98-103
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• 2000

This paper is concerned with a delay-dependent sliding mode scheme for the robust stabilization of input-delay systems with bounded unknown uncertainties. A sliding surface based ona predictor is proposed to minimize the effect of the input delay. Then, a robust control law is derived to ensure the existence of a sliding mode on the surface. In input-delay systems, uncertainties given during te delayed time are not directly controlled by the switching control because of causality prolem of them. They can influence the stability of the system in the sliding mode. Hence, a delay-dependent stability analysis for reduced order dynamics is employed to estimate maximum delay bound such that the system is globally asymptotically stable in the sliding mode. A numerical example is given to illustrate the design procedure.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.68-74
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• 1998

The neural network predictiv econtroller (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 (NNP:neural network predictor) and the other one is for control the plant(NNC: neural network controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and predictin error. The NNP forecasts the future output based upon the current control input and the estimated control output. The input and the output data of a system and a new method using evolution strategy are used to train the NNP. A two-step NNPC is applied to control the temeprature in boiler systems. It was compared with PI controller and auto-tuning PID controller. The computer simulaton and experimental results show that the proposed method has better performances than the other method.