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Bin Yang,Zhenxing Liu,Hui-Kang Liu,Yan Li,Sen Lin 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.10
It is one of the key tasks for the bridge crane to achieve anti-swing control of the hook and the accurate positioning of the body to work efficiently, safely and automatically. Based on the Lagrange equation, this paper is to propose a dynamic model of the crane motion system for designing controller. In the controller design, ProportionalIntegral-Derivative (PID), the most widely used controller in engineering, is adopted and a new parameter tuning algorithm for a multi-variable PID controller based on generalized predictive control (GPC) is given. It is foundthat the multi-variable PID controller shares the same structural mathematical expressions with the GPC law, which makes for the transfer and calculation of the three parameters P, I and D, and that the new algorithm enables the traditional PID controller to perform as brilliantly as the GPC. The results of both the simulation and real-time control experiments show that the newly-proposed PID controller can effectively eliminate the swing of the hook and control the bridge cranes moving position accurately.
Generalized Model Predictive Control for a Multivariable Boiler- Turbine Unit
Hossein Reza Karampoorian,Reza Mohseni 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
In this paper, a multivariable boiler- turbine unit is controlled using Generalized Model Predictive Control (GPC). This control strategy for multivariable plant can achieve high performance and more effective than the traditional PID controllers. Also, predictive controllers for MIMO process does not need to retuning the parameters. The simulation results show that the tracking perfomance the desired values of the state variables . Furthermore, the disturbance rejection capability of GPC are investigated.
일반형예측제어를 이용한 PID 이득 선정 및 자기동조 PID 제어기 설계
윤강섭 대구대학교 산업기술연구소 2009 産業技術硏究 Vol.20 No.1
The predictive controls, based on a predictive control strategy, have emerged as a powerful practical control technique especially in the process industries. Predictive control strategy is to predict the effects of control inputs on the future values of plant outputs and to find the best control inputs which minimize the deviation of the predicted outputs from the desired outputs. Based on the underlying model, predictive control can be divided into three branches; Model Predictive Control(MPC), Generalized Predictive Control(GPC), and Receding Horizon Control(RHC). MPC use the Finite Impulse Response(FIR) and Finite Step Response(FSR) model, respectively. GPC use mainly the SISO I/O models such as CARMA or CARIMA models. RHC use the state space models. PID control scheme has been widely used for control of real systems. Particularly, the issues regarding on tuning PID gains of the control scheme have been studied by many researchers. However, to the best of our knowledge, there is no result for discrete-time systems with unknown time-delay and unknown system parameters. In this paper, self-tuning PID control algorithm for unknown parameters and unknown time-delay system is proposed. Simulation results have been presented to illustrate the effectiveness of the proposed methods.
Finite Memory Generalized Predictive Controls for Discrete-time State Space Models
Jung Hun Park,Soohee Han,Wook Hyun Kwon 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In this paper, a generalized predictive control (GPC) is represented over state space and then is shown to be separated into the receding horizon control (RHC) and the steady-state Kalman filter. By utilizing only the information on the recent finite inputs and outputs, we propose a new finite memory GPC (FMGPC) that consists of the RHC and a finite impulse response (FIR) filter. The proposed FMGPC will be compared with a conventional GPC for an input-output (I/O) model and the existing receding horizon finite memory control (RHFMC) for a state space model.
Fuzzy Neural Network Based Generalized Predictive Control of Chaotic Nonlinear Systems
Park, Jong-Tae,Park, Yoon-Ho The Korean Institute of Electrical Engineers 2004 전기학회논문지 D Vol.53 No.2
This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.
Damping of Inter-Area Low Frequency Oscillation Using an Adaptive Wide-Area Damping Controller
Wei Yao,L. Jiang,Jiakun Fang,Jinyu Wen,Shaorong Wang 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.1
This paper presents an adaptive wide-area damping controller (WADC) based on generalized predictive control (GPC) and model identification for damping the inter-area low frequency oscillations in large-scale inter-connected power system. A recursive least-squares algorithm (RLSA) with a varying forgetting factor is applied to identify online the reduced-order linearlized model which contains dominant inter-area low frequency oscillations. Based on this linearlized model, the generalized predictive control scheme considering control output constraints is employed to obtain the optimal control signal in each sampling interval. Case studies are undertaken on a two-area fourmachine power system and the New England 10-machine 39-bus power system, respectively. Simulation results show that the proposed adaptive WADC not only can damp the inter-area oscillations effectively under a wide range of operation conditions and different disturbances, but also has better robustness against to the time delay existing in the remote signals. The comparison studies with the conventional lead-lag WADC are also provided.
Hossein Reza Karampoorian,Reza Mohseni 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
In this paper, we designed generalized model predictive control for linearized magnetic levitation system and applied to the nonlinear system. The proposed control strategy is guarantee the stability of the open loop unstable system and tracking the desired values of the state variables with the stabilizing constraints.The simulation results show that the tracking performance and effectiveness of generalized predictive control strategy. Furthermore, robustness with respect to mismatch between model/process with changes in the parameters of the system are investigated.
Constrained Generalized Predictive Control Of An Induction Motor
Bektache Abdeldjebar,Benmahammed Khier 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
A constrained generalized predictive control (GPC) algorithm is presented for the non linear system. Generalized predictive control methods are gaining widespread acceptance industry-as they offer good performance based on simple step response or transfer -function which can be obtained experimentally a GPC.In this paper we are illustrated the GPC without constraint and output constrained controller to induction motor drive. The variable to be controlled are the rotor speed and flux trajectory .The load torque is considered as unknown disturbance. The simulation results show a good performance for the non linear system.
A new control strategy for induction motor based on linear predictive control with constraints
Bektache Abdeldjebar 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
A constrained generalized predictive control(GPC) algorithm is presented for the nonlinear system. Gener-alized predictive control methods are gaining wide spread acceptance industry-as they offer good performance based on simple step response or transfer-function which can be obtained experimentally a GPC. In this paper weare illustrated the GPC without constraint and output constrained controller to induction motor drive. The variable to be controlled are the rotor speed and?uxtrajectory. The load to rque is considered as unknown disturbance. The simulation results show a good performance for the nonlinear systems.
세라믹 건조로 온도 제어를 위한 가중계수를 갖는 일반형 예측제어
임태규,성원준,금영탁,송창섭 한국결정성장학회 2003 韓國結晶成長學會誌 Vol.13 No.6
The electric furnace, inside which the desired temperature is kept by the generated heat, is known to be a difficult system to control and model exactly because system parameters and response delayed time are varied as the temperature and positions are changed. In this study, the GPCEW (generalized predictive control with exponential weight), which always guarantees the stability of the closed loop system and can be effectively applied to the internally unstable system, was introduced to the ceramic drying electric furnace and was verified by showing its temperature tracking performance experimentally. 내부에 열을 가하여 원하는 온도를 유지하는 전기로는, 정확하게 제어하고 모델링을 하기 힘든 시스템이다. 왜냐하면 시스템 변수와 응답 지연 시간이 온도와 위치가 변함에 따라 변하기 때문이다. 이번 연구에서 항상 폐루프 시스템에서 안정성을 보증하고, 내부가 불안정한 시스템에 효과적으로 적용될 수 있는 가중계수를 갖는 일반형 예측 제어가 세라믹 전기로에 적용되었고, 실험을 통해 온도 추적 이행을 보임으로서 확인하였다.