• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1527-1532
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• 2003

기존의 GPC방법으로 제어하기 힘든 비선형성과 플랜트의 변수변화를 포함하는 비선형 플랜트를 지연 예측신경망을 사용하여 효과적으로 제어하는 적응 GPC방법을 제안한다 제안한 방법에서는 플랜트의 선형 변수 추정이나 근사적인 모델로부터 선형 매개변수를 구해서 선형 모델을 만들고 실제 시스템의 출력과 선형모델의 오차를 신경망의 출력으로 표현한 다음, 이 식으로부터 적응 GPC 알고리듬을 유도한다. 여기서 지연 예측신경망은 적응 GPC에 이용될 플랜트의 출력을 예측하도록 학습된다. 이와 같은 제어기를 구성함으로써 선형 변수만으로 적응 GPC 제어기가 구성되어질 경우 생기는 비선형 변수의 추정과 출력 예측 값을 계산하는 번거로움을 해결하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.97-105
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• 1996

In this paper, an adaptive model predictive controller for nodinear processes using fuzzy model is proposed. Adaptive structure is implemented by recursive fuzzy modeling. The model and control law can be obtained the same as GPC, because the consequent parts of the fuzzy model comprise linear equations of input and output variables. The proposed Adaptive fuzzy model predictive controller (AFMPC) controls nonlinear process well due to the intrinsic nonlinearity of the fuzzy model. When AFMPC's output is variation in the process control input, it maintains zero steady-state offset for a constant reference input and has superior performance. The properties and performance of the proposed control scheme were examined with nonlinear plant by simulation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.73-79
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• 2009

For the shake of control for movement object, control theory like neural network, nonlinear model predictive control(NMPC) is realized on digital high speed computer. Predictor of flight control system(FCS) based nonlinear model predictive control has to be satisfied with response for hard real-time to perform applications on each module in the FCS. Simultaneously, It gives a serious consideration accuracy to give full play to FCS's performance. Error of mathematical aspect affects realization of whole algorithm. But factors of bring mathematical error is not considered to calculate final accuracy on parameter of predictor. In this paper, Predictor was made using load control model on the digital computer for design FCS at hard real-time and is shown response time on realization algorithm. And is shown realization algorithm of high effective predictor over the accuracy. The predictor was realized on the load control model using Euler method, Heun method, Runge-Kutta and Taylor method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.85-90
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.35-41
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• 2010

In developing modern aircraft, the reconfiguration control that can improve the safety and the survivability against the unexpected failure by partitioning control surfaces into several parts has been actively studied. This paper deals with the reconfiguration control using model predictive control method considering the saturation of control surfaces under the control surface failure. Linearized aircraft model at trim condition is used as the internal model of model predictive control. We propose the controller that performs optimization using LMI (linear matrix inequalities) based semi-definite programming in case that control surface saturation occurs, otherwise, uses analytic solution of the model predictive control. The performance of the proposed control method is evaluated by nonlinear simulation under the flight scenario of control surface failure.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.2
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• pp.20-30
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• 2001

In the paper, we propose a predictive control scheme using multiple neural network-based prediction models. To construct the multiple models, we select several specific values of a parameter whose variation affects serious control performance in the plant. Among the multiple prediction models, we choose one that shows the best predictions for future outputs of the plant by a switching technique. Based on a nonlinear programming method, we calculate the current process input in the nonlinear predictive control system with multiple prediction models. The proposed control method is shown to be very effective when a parameter of the plant changes or the time delay, if it exists, varies. It is also shown that the proposed method is successfully applied for the control of suspension in a electro-magnetic levitation system.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.54-64
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• 2005

The study investigated genetic algorithms for the optimal control model of maximum height vertical jumping. The model includes forward dynamic simulations by the neural excitation-control variables. Convergence of genetic algorithms is very slow. In this paper the micro genetic algorithm(micro-GA) was used to reduce the computation time. Then a near optimal solution from micro-GA was an initial solution for VF02, which is one of well-developed and proven nonlinear programming algorithms. This approach provided the successful optimal solution for maximum-height jumping without a reasonable initial guess.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.448-449
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• 2008

본 논문에서는 이동 로봇의 경로 추종을 위해서, 비선형 모델 예측 슬라이딩 모드 제어(nonlinear model predictive strung mode control) 기법을 제안한다. 본 논문에서 제안한 방법에서는 미래의 슬라이딩 평면을 예측하고, 이에 따른 최적화된 제어기를 유도함으로써 슬라이딩 모드 제어기 단독으로 사용하는 제언 시스템에 비해 성능을 향상시킬 수 있다. 마지막으로 컴퓨터 시뮬레이션을 통해 본 논문에서 제안한 제어기의 성능을 검증하고자한다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.2
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• pp.55-59
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• 2010

Recently it is tried to use load control model for maneuver moving object. MIN design method proposed to solve control problem of nonlinear system using load concept. The Min design method shows direct method for finding control value on the load control model. In this paper, is shown realization free falling model using nonlinear load control model and analysis of load values acting falling object according to disturbance. And made a trajectory according to acting load values due to disturbance. This paper's result is able to be applied to design algorithm for improvement accuracy of MLRS, GPS air-to-surface missile(ASM) and returning spacecraft with nonlinear model predictive control.