• Title/Summary/Keyword: model predictive control

검색결과 559건 처리시간 0.034초

연료전지 시스템을 위한 헤머스테인-위너 모델기반의 모델예측제어 (Hammerstein-Wiener Model based Model Predictive Control for Fuel Cell Systems)

  • 이상문
    • 전기학회논문지
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    • 제60권2호
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    • pp.383-388
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    • 2011
  • In this paper, we consider Hammerstein-Wiener nonlinear model for solid oxide fuel cell (SOFC). A nonlinear model predictive control (MPC) is proposed to trace the constant stack terminal power by Hydrogen flow as control input. After the stability of the closed-loop system with static output feedback controller is analysed by Lyapunov method, a nonlinear model predictive control based on the Hammerstein-Wiener model is developed to control the stack terminal power of the SOFC system. Simulation results verify the effectiveness of the proposed control method based on the Hammerstein-Wiener model for SOFC system.

일반제한조건의 이동로봇예측제어기 최적화 (Optimization of Mobile Robot Predictive Controllers Under General Constraints)

  • 박진현;최영규
    • 한국정보통신학회논문지
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    • 제22권4호
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    • pp.602-610
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    • 2018
  • 모델예측제어는 기준 궤적이 알려져 있을 경우 제어시스템의 예측모델을 이용하여 현재 제어상태 및 미래오차 등을 예측하여 현재 제어입력을 최적화시킬 수 있는 효과적인 방법이다. 모바일로봇의 제어입력이 물리적으로 무한히 큰 값을 가질 수 없으므로 제한조건을 갖는 예측제어기 설계가 고려되어야 한다. 또한 예측제어기의 제어성능을 결정하는 기준모델행렬 $A_r$과 가중치행렬 Q, R들이 임의로 설정됨에 따라 성능이 최적화되지 못한 부분도 설계에 고려되어야 한다. 본 연구에서는 제한조건을 갖는 quadratic programming 문제로 변형하여 모바일로봇의 예측제어기를 구성하고, 모바일 로봇의 제어성능을 결정하는 예측제어기의 제어파라미터인 기준모델행렬 $A_r$과 가중치행렬 Q, R에 대하여 유전알고리즘을 적용하여 제어파라미터들을 최적화함으로써 제어성능을 높일 수 있었다. 컴퓨터 모의실험을 통하여 본 연구에서 제안한 제어방법이 기존의 예측제어기의 추종성능보다 뛰어남을 확인하고자한다.

모델예측제어 기법을 이용한 제지공정에서의 지종교체 제어 (Control of Grade Change Operations in Paper Plants Using Model Predictive Control Method)

  • 김도훈;여영구;박시한;강홍
    • 펄프종이기술
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    • 제35권4호
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    • pp.48-56
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    • 2003
  • In this work an integrated model for paper plants combining wet-end and dry section is developed and a model predictive control scheme based on the plant model is proposed. Closed-loop process identification method is employed to produce a state-space model. Thick stock, filler flow, machine speed and steam pressure are selected as input variables and basis weight, ash content and moisture content are considered as output variables. The desired output trajectory is constructed in the form of 1st-order dynamics. Results of simulations for control of grade change operations are compared with plant operation data collected during the grade change operations under the same conditions as in simulations. From the comparison, we can see that the proposed model predictive control scheme reduces the grade change time and achieves stable steady-state.

예측제어기법을 이용한 PID 제어기 설계 (The PID Controller for Predictive control Algorithm)

  • 김양환;이정재;이정용;이장명
    • 제어로봇시스템학회논문지
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    • 제11권1호
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    • pp.19-26
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    • 2005
  • This paper is concerned with the design of a predictive PID controller which has similar features to the model-based predictive controller. A PID type control structure is defined, which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are precalculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with the conventional PID and fuzzy control algorithms.

Advances in Nonlinear Predictive Control: A Survey on Stability and Optimality

  • Kwon, Wook-Hyun;Han, Soo-Hee;Ahn, Choon-Ki
    • 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.

Fault-Tolerant Control for 5L-HNPC Inverter-Fed Induction Motor Drives with Finite Control Set Model Predictive Control Based on Hierarchical Optimization

  • Li, Chunjie;Wang, Guifeng;Li, Fei;Li, Hongmei;Xia, Zhenglong;Liu, Zhan
    • Journal of Power Electronics
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    • 제19권4호
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    • pp.989-999
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    • 2019
  • This paper proposes a fault-tolerant control strategy with finite control set model predictive control (FCS-MPC) based on hierarchical optimization for five-level H-bridge neutral-point-clamped (5L-HNPC) inverter-fed induction motor drives. Fault-tolerant operation is analyzed, and the fault-tolerant control algorithm is improved. Adopting FCS-MPC based on hierarchical optimization, where the voltage is used as the controlled objective, called model predictive voltage control (MPVC), the postfault controller is simplified as a two layer control. The first layer is the voltage jump limit, and the second layer is the voltage following control, which adopts the optimal control strategy to ensure the current following performance and uniqueness of the optimal solution. Finally, simulation and experimental results verify that 5L-HNPC inverter-fed induction motor drives have strong fault tolerant capability and that the FCS-MPVC based on hierarchical optimization is feasible.

비선형 시스템을 위한 퍼지모델 기반 일반예측제어 (Fuzzy Model Based Generalized Predictive Control for Nonlinear System)

  • 이철희;서선학
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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    • pp.697-699
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    • 2000
  • In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

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에너지효율을 고려한 모델예측제어에 기초한 열펌프의 실내온도 제어 (Indoor Temperature Control of a Heat Pump Based on Model Predictive Control Considering Energy Efficiency)

  • 조항철;변경석;송재복;장효환;최영돈
    • 설비공학논문집
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    • 제13권3호
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    • pp.200-208
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    • 2001
  • In indoor temperature control of a heat pump, a reduction in energy consumption is very important. However, most control schemes for heat pumps have focused only on control performance such s settling time and steady-state error. In this paper, the model predictive control (MPC) which includes the energy-related variable in this cost function is proposed. By computing the control signal minimizing this cost function, the trade-off between energy reduction and temperature control performance can be obtained. Since the MPC required the process model, the dynamic mode of a heat pump is also obtained by the system identification technique. Performance of the proposed MPC considering energy efficiency is compared with the two other control schemes. It si shown that the proposed scheme can consume less energy thant hte others in achieving similar control performance.

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Robust Predictive Control of Robot Manipulator with The Bound Estimation

  • Kim, Jung-Kwan;Han, Myung-Chul
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2001년도 ICCAS
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    • pp.155.5-155
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    • 2001
  • The robust predictive control law which use the bound estimation is proposed for uncertain robot manipulators. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about model, it´s an important tend to design a robust control law that will guarantee the desired performance of the manipulator under uncertain elements. In the preceeding work, the robust predictive control law was proposed. In this work, we propose a class of robust predictive control of manipulators with the bound estimate technique and fe stability based on Lyapunov function is presented.

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입력 가중치를 이용한 예측제어 (Predictive controller using weighted input)

  • 나상섭;신세희;어영구
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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    • pp.343-347
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    • 1989
  • In this paper, predictive control method using actual applied input which is the weighted summation of past inputs is presented. In conventional predictive control methods, a set of control inputs is computed and in these only the first element is applied to the process at each time instant. But this predictive control method based on conventional methods considers all computed control inputs. Consequently, the characteristic of response and the reliability of the control scheme in the case of imperfact model are improved.

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