• 전자공학회논문지SC
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• 제39권1호
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• pp.51-65
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• 2002

본 논문에서는 혼돈 비선형 시스템의 지능 제어를 위해 간접 적응 제어 방식에 기초한 신경 회로망 제어기 설계 방법을 제안하였다. 제안된 제어 방법은 혼돈 비선형 시스템의 동정을 위해 다층 신경 회로망과 간단한 상태 공간 신경 회로망을 사용한 직-병렬 동정 구조를 이용하여 오프 라인으로 동정 과정을 수행하였으며, 학습된 혼돈 비선형 시스템에 대한 신경 회로망 모델을 사용하여 온 라인으로 제어를 수행하였다. 이때 혼돈 비선형 시스템의 동정 및 제어를 위한 학습 방법은 오차 역전파 방법을 사용하였다. 한편 본 논문에서 제안된 제어 방법을 연속 시간 혼돈 비선형 시스템인 Duffing 방정식과 Lorenz 방정식에 각각 적용하여 신경 회로망을 사용한 기존의 제어 방법과 컴퓨터 모의 실험을 통해 제어 성능을 비교 및 고찰하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권8호
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• pp.495-497
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.105-109
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• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• Nuclear Engineering and Technology
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• 제55권10호
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• pp.3515-3524
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• 2023

A reinforcement learning framework is proposed for the control problem of outlet steam pressure of the once-through steam generator(OTSG) in this paper. The double-layer controller using Proximal Policy Optimization(PPO) algorithm is applied in the control structure of the OTSG. The PPO algorithm can train the neural networks continuously according to the process of interaction with the environment and then the trained controller can realize better control for the OTSG. Meanwhile, reinforcement learning has the characteristic of difficult application in real-world objects, this paper proposes an innovative pretraining method to solve this problem. The difficulty in the application of reinforcement learning lies in training. The optimal strategy of each step is summed up through trial and error, and the training cost is very high. In this paper, the LSTM model is adopted as the training environment for pretraining, which saves training time and improves efficiency. The experimental results show that this method can realize the self-adjustment of control parameters under various working conditions, and the control effect has the advantages of small overshoot, fast stabilization speed, and strong adaptive ability.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.93-100
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• 2003

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

• 품질경영학회지
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• 제38권2호
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• pp.190-201
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• 2010

This research proposes a method for designing VSI (Variable Sampling Interval) control charts with supplementary run rules. The basic idea is to apply various run rules and the VSI scheme to a control chart in order to increase the sensitivity. The sampling process of the VSI run rules chart is constructed by Markov chain approach. A procedure for designing the VSI run rules chart is proposed based on Lorenzen and Vance's model. Sensitivity study shows that the VSI run rules charts outperform the FSI (Fixed Sampling Interval) run rules charts for wide range of process mean shifts. A major advantage of the VSI run rules chart over other charts such as CUSUM, EWMA, and adaptive charts is it's simplicity in implementation. Some useful guidelines are proposed based on the sensitivity study.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.702-704
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• 1997

In this paper, we applied self-tuning controller with I-PD type to process with time delay's. Process parameters are estimated by the recursive least squares algorithm, and optimal gains are obtained. This paper shows self-tuning controller with I-PD type performs better than that of general PID type for the nonlinear system with sudden change of set-points.

• 한국통신학회논문지
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• 제22권4호
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• pp.675-684
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• 1997

The bit rate control algorithm which is capable of handing scene change is proposed. In MPEG-2 TM5, block variance is used to measure block activity. But block variance is not consistent with human visual system and does not differenciate the distribution of pixel values within the block. In target bit allocation process of TM5, global complexity, obtained by results of previous coded pictures, is used. Since I pictures are spaced relatively far apart, their complexity estimate is not very accurate. In the proposed algorithm local variance is used to measure block activity and detect scene change. Local variance, using deviation from the mean of neighboring pixels, well represents the distribution of pixel values within the block. If scene change is detected, the local variance information is used for target bit allocation process. Allocating target bits for I picture, the average local variance difference between previous and current I picture is considered. The experimental results show that the proposed algorithm can detect scene change very precisely and gives better picture quality and higher PSNR values than MPEG-2 TM5.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.