• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.49-55
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• 1992

In this paper, we developed a Receding Horizon Predictive Control for Stochastic state space models(RHPCS). RHPCS was designed to minimize a quadratic cost function. RHPCS consists of Receding Horizon Tracking Control(RHTC) and a state observer. It was shown that RHPCS is equivalent to Generalized Predictive Control(GPC) when the underlying state space model is equivalent to the I/O model used in the design of GPC. The equivalence between GPC and RHPCS was shown through. the comparison of the transfer functions of the two controllers. RHPCS provides a time-invarient optimal control law for systems for which GPC can not be used. The stability properties of RHPCS was derived. From the GPC's equivalence to RHPCS, the stability properties of GPC were shown to be the same as those for RHTC.

• 대한전자공학회논문지
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• 제25권9호
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• pp.1039-1045
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• 1988

In the original incremental generalized predictive control, the receding horizon predictive control is introduced as a control law. But in this paper, we propose a generalized predictive self-tuning control using full-valued incremental controls. The control law is a mean horizon predictive control. The effectiveness of this algorithm in a variable time delay or load disturbances environment is demonstrated by computer simulation. The controlled plant is a nonminimum phase system.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.231-238
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• 2023

부스트 PFC (Power Factor Correction)컨버터는 AC 입력 전류의 단일 역률과 낮은 THD (Total Harmonic Distortion)를 달성하기 위해 다양한 제어기법들이 연구되고 있다. 그중 인덕터 전류의 평균값을 전류지령에 추종하도록 제어하는 평균전류 모드 제어가 있으며 가장 널리 사용되고 있다. 하지만, 오늘날 디지털 프로세서의 발달로 고도화된 디지털 제어가 가능해지면서 부스트 PFC 컨버터의 예측제어가 관심을 받고 있다. 예측제어에는 예측 알고리즘으로 듀티를 미리 생성하는 예측전류 모드 제어 및 모델을 기반으로 한 비용함수를 선정하여 스위칭 동작을 하는 모델예측제어로 분류된다. 따라서 본 논문에서는 부스트 PFC 컨버터의 평균전류 모드 제어, 예측전류 모드 제어, 모델예측 전류 제어를 간단히 설명한다. 또한, 시뮬레이션을 통해 전체 부하 및 다양한 외란 조건에서의 전류 제어를 비교 분석한다.

• 지능정보연구
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• 제5권1호
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• pp.35-48
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• 1999

EDI통제를 설계할 때에는 통제의 효과성 및 효율성을 위해서 조직 및 상황특성을 고려하여야 한다. 본 논문은 특정 환경적 특성을 가지는 조직에 대해 EDI통제 수준을 제안하는 인공선경망 모형을 제안한다. 환경적 확성으로부터 12가지 종류의 EDI통제에 대한 수준을 각각 제안하도록 127개의 역전파 인공신경망 모형이 설계되었다. 본 논문에서 제시한 모형의 효과성을 검증하기 위하여 인공 신경망의 예측력을 다종회귀분석과 비교되었다 예측력 비교결과 인공신경망 모형의 예측력이 다중 회귀분석보다 우수함이 입증되었다. 인공신경망을 활용하여 과거의 환경 및 통제수준에 관한 데이 터를 학습하여 통제설계에 관한 보다 일관되고 체계적인 의사결정을 할 수 있을 것이다. 또한 보다 높은 수준이 요구되는 통제에 대하여 EDI 관리자나 내부감사인들은 그들의 한정된 자원을 투입하여 구현하도록 할 수 있을 것이다.

• KIEAE Journal
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• 제15권4호
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• pp.105-110
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• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• 동력기계공학회지
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• 제6권3호
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• pp.71-77
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• 2002

In this study, a dynamic model of proportional pressure control valve using the bond graph and a predictive controller are presented in the form of dynamic matrix control which is concerned with a design method of digital controller for the electro hydraulic servo system. The bond graph can be utilized for all types of systems which involve power and energy, and it is applied to a propotional pressure control valve in this study. Recently, many researchers suggested that better control performance could be obtained by means of the predictive controls with future reference input, future control output and future control error. The Predictive controller is very practical because the controller can be easily applicable to a personal computer or a microprocessor. This study investigates through numerical simulations that hydraulic system with the predictive controller shows very good control performances.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.15-22
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• 2004

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.1138-1145
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• 2013

A novel predictive current control strategy for a sparse matrix converter is presented. The sparse matrix converter is functionally-equivalent to the direct matrix converter but has a reduced number of switches. The predictive current control uses a model of the system to predict the future value of the load current and generates the reference voltage vector that minimizes a given cost function so that space vector modulation is achieved. The results show that the proposed controller for sparse matrix converters controls the load current very effectively and performs very well through simulation and experimental results.

• 대한전기학회논문지
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• 제43권4호
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• pp.627-637
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• 1994

In this study, a predictive fuzzy control algorithm to supervise the elevator system with plural cars is developed and its performance is evaluated. The proposed algorithm is based on fuzzy in-ference system to cope with multiple control objects and uncertainty of system state. The control objects are represented as linguistic predictive fuzzy rules and simplified reasoning method is utilized as a fuzzy inference method. Real-time simulation is performed with respect o all possible modes of control, and the resultant controls ard predicted. The predicted rusults are then utilized as the control in-puts of the fuzzy rules. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer. Finallu, the results of graphic simulation is compared with those of a conventional group control algorighm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.801-806
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• 1987

The receding horizon tracking control for the discrete time invariant systems is presented in this paper. This control law is derived with the receding horizon concept from the standard tracking problems. Stability properties of this control law are analyzed. It is shown that there exists a finite horizon index for which the closed loop systems are always asymptotically stable. The receding horizon tracking control is a kind of predictive control and will add a new clan to many existing predictive controls, with which some comparisons are made.