• Title, Summary, Keyword: Predictive Control

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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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    • v.2 no.1
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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.

Design of Model Predictive Controllers with Velocity and Acceleration Constraints (속도 및 가속도 제한조건을 갖는 모델예측제어기 설계)

  • Park, Jin-Hyun;Choi, Young-Kiu
    • Journal of the Korean Society of Mechanical Technology
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    • v.20 no.6
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    • pp.809-817
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    • 2018
  • The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

The application of model predictive control for multi-loop control structure (다중루프 제어구조에의 모델예측제어의 적용)

  • 문혜진;이광순
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1400-1403
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    • 1996
  • In this study, we applied the model predictive control(MPC) to Multi-loop control structure. Since MPC has many advantage for MIMO process and constraints handling, it induces the better performance to apply MPC to multi-loop control. And we suggest the advanced method to reduce the calculation load using the wavelet transform. It shows the possibility to substitute the existing PID control based structure with MPC.

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Double-Objective Finite Control Set Model-Free Predictive Control with DSVM for PMSM Drives

  • Zhao, Beishi;Li, Hongmei;Mao, Jingkui
    • Journal of Power Electronics
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    • v.19 no.1
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    • pp.168-178
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    • 2019
  • Discrete space vector modulation (DSVM) is an effective method to improve the steady-state performance of the finite control set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set model-free predictive control using DSVM to reduce the computational burden. First, model-free deadbeat current control is used to generate the reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function. Additionally, in order to implement double-objective control, a two-level decisional cost function is designed to sequentially reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is validated based on experimental tests.

Model predictive control strategies for protection of structures during earthquakes

  • Xu, Long-He;Li, Zhong-Xian
    • Structural Engineering and Mechanics
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    • v.40 no.2
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    • pp.233-243
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    • 2011
  • This paper presents a theoretical study of a model predictive control (MPC) strategy employed in semi-active control system with magnetorheological (MR) dampers to reduce the responses of seismically excited structures. The MPC scheme is based on a prediction model of the system response to obtain the control actions by minimizing an objective function, which can compensate for the effect of time delay that occurred in real application. As an example, a 5-story building frame equipped with two 20 kN MR dampers is presented to demonstrate the performance of the proposed MPC scheme for addressing time delay and reducing the structural responses under different earthquakes, in which the predictive length l = 5 and the delayed time step d = 10, 20, 40, 60, 100 are considered. Comparison with passive-off, passive-on, and linear quadratic Gaussian (LQG) control strategy indicates that MPC scheme exhibits good control performance similar to the LQG control strategy, both have better control effectiveness than two passive control methods for most cases, and the MPC scheme used in semi-active control system show more effectiveness and robustness for addressing time delay and protecting structures during earthquakes.

Accurate Position Control of Hydraulic Motor Using NNGPC (NNGPC를 이용한 유압모터의 고정도 위치제어)

  • 박동재;안경관;이수한
    • 제어로봇시스템학회:학술대회논문집
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    • pp.143-143
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    • 2000
  • A neural net based generalized predictive control(NNGPC) is presented for a hydraulic servo position control system. The proposed scheme employs generalized predictive control, where the future output being generated from the output of artificial neural networks. The proposed NNGPC does not require an accurate mathematical model for the nonlinear hydraulic system and takes less calculation time than GPC algorithm if the teaming of neural network is done. Simulation studies have been conducted on the position control of a hydraulic motor to validate and illustrate the proposed method.

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A Study on Predictive Fuzzy Control Algorithm for Elevator Group Supervisory System (엘리버이터 군관리 시스템을 위한 예견퍼지 제어 알고리즘에 관한 연구)

  • Choi, Don;Park, Hee-Chul;Woo, Kang-Bang
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.43 no.4
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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.

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Performance Improvement of Model Predictive Control Using Control Error Compensation for Power Electronic Converters Based on the Lyapunov Function

  • Du, Guiping;Liu, Zhifei;Du, Fada;Li, Jiajian
    • Journal of Power Electronics
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    • v.17 no.4
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    • pp.983-990
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    • 2017
  • This paper proposes a model predictive control based on the discrete Lyapunov function to improve the performance of power electronic converters. The proposed control technique, based on the finite control set model predictive control (FCS-MPC), defines a cost function for the control law which is determined under the Lyapunov stability theorem with a control error compensation. The steady state and dynamic performance of the proposed control strategy has been tested under a single phase AC/DC voltage source rectifier (S-VSR). Experimental results demonstrate that the proposed control strategy not only offers global stability and good robustness but also leads to a high quality sinusoidal current with a reasonably low total harmonic distortion (THD) and a fast dynamic response under linear loads.

Predictive and Preventive Maintenance using Distributed Control on LonWorks/IP Network

  • Song, Ki-Won
    • International Journal of Safety
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    • v.5 no.2
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    • pp.6-11
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    • 2006
  • The time delay in servo control on LonWorks/IP Virtual Device Network (VDN) is highly stochastic in nature. LonWorks/IP VDN induced time delay deteriorates the performance and stability of the real-time distributed control system and hinders an effective preventive and predictive maintenance. Especially in real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. In order to guarantee the stability and performance of the system for effective preventive and predictive maintenance, LonWorks/IP VDN induced time delay needs to be predicted and compensated for. In this paper position control simulation of DC servo motor using Zero Phase Error Tracking Controller (ZPETC) as a feedforward controller, and Internal Model Controllers (IMC) based on Smith predictor with disturbance observer as a feedback controller is performed. The validity of the proposed control scheme is demonstrated by comparing the IMC based on Smith predictor with disturbance observer.

Predictive Control for a Fin Stabilizer

  • Yoon, Hyeon-Kyu;Lee, Gyeong-Joong;Fang, Tae-Hyun
    • Journal of Navigation and Port Research
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    • v.31 no.7
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    • pp.597-603
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    • 2007
  • A predictive controller can solve a control problem related to a disturbance-dominant system such as roll stabilization of a ship in waves. In this paper, a predictive controller is developed for a fin stabilizer. Future wave-induced moment is modeled simply using two typical regular wave components for which six parameters are identified by the recursive Fourier transform and the least squares method using the past time series of the roll motion. After predicting the future wave-induced moment, optimal control theory is applied to discover the most effective command fin angle that will stabilize the roll motion. In the results, wave prediction performance is investigated, and the effectiveness of the predictive controller is compared to a conventional PD controller.