A design method of generalized predictive control system based on parametrization of two-degree-of-freedom integral controllers

2002 ◽  
Author(s):  
A. Naganawa ◽  
G. Obinata ◽  
K. Aida
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Design Method ◽  
Degree Of Freedom ◽  
Generalized Predictive Control ◽  
Two Degree Of Freedom

A robustification of the two degree-of-freedom controller based upon multivariable generalized predictive control law and robust H∞ control for a doubly-fed induction generator

2016 ◽  
Vol 40 (3) ◽  
pp. 1005-1017 ◽  
Author(s):  
Mohammed Aidoud ◽  
Moussa Sedraoui ◽  
Abderrazek Lachouri ◽  
Abdelhalim Boualleg
Keyword(s):  
Wind Turbine ◽  
Predictive Control ◽  
Degree Of Freedom ◽  
Induction Generator ◽  
Generalized Predictive Control ◽  
Doubly Fed Induction Generator ◽  
Wind Turbine System ◽  
Youla Parameterization ◽  
Doubly Fed ◽  
Two Degree Of Freedom

A robustification method of primary two degree-of-freedom (2-DOF) controllers is proposed in this paper to control the wind turbine system equipped with a doubly-fed induction generator DFIG. The proposed robustification method should follow the following three step-procedures. First, the primary 2-DOF controller is designed through the initial form of the multivariable generalized predictive control MGPC law to ensure a good tracking dynamic of reference trajectories. Second, the robust [Formula: see text] controller is independently designed for the previous system to ensure good robustness properties of the closed-loop system against model uncertainties, neglecting dynamics and sensor noises. Finally, both above mentioned controllers are combined to design the robustified 2-DOF-MGPC controller using Youla parameterization method. Therefore, the obtained controller conserves the same good tracking dynamic that is provided by the primary 2-DOF-MGPC controller. It ensures the same good robustness properties which are produced by the robust [Formula: see text] controller. A wind turbine system equipped with a DFIG is controlled by the robustified 2-DOF-MGPC controller. Its dynamic behaviour is modelled by an unstructured-output multiplicative uncertainty plant. The controller performances are valid by comparison with those given through both controllers, which are primary 2-DOF-MGPC and robust [Formula: see text] controllers in time and frequency domains.

A Design Method for Two-Degree-of-Freedom Simple Multi-Period Repetitive Control Systems

2010 ◽  
Vol 36 ◽  
pp. 243-252 ◽  
Author(s):  
Yoshinori Ando ◽  
Tatsuya Sakanushi ◽  
Kou Yamada ◽  
Iwanori Murakami ◽  
Takaaki Hagiwara ◽  
...  
Keyword(s):  
Control System ◽  
Control Systems ◽  
Transfer Functions ◽  
Design Method ◽  
Output Characteristic ◽  
Degree Of Freedom ◽  
Disturbance Attenuation ◽  
Input Output ◽  
Attenuation Characteristic ◽  
Two Degree Of Freedom

The multi-period repetitive (MPR) control system is a type of servomechanism for periodic reference inputs. Using MPR controllers, transfer functions from the reference input to the output and from the disturbance to the output of the MPR control system have infinite numbers of poles. To specify the input-output characteristic and the disturbance attenuation characteristic easily, Yamada and Takenaga proposed MPR control systems, named simple multi-period repetitive (simple MPR) control systems, where these transfer functions have finite numbers of poles. In addition, Yamada and Takenaga clarified the parameterization of all stabilizing simple MPR controllers. However, using the simple MPR repetitive controller by Yamada and Takenaga, we cannot specify the input-output characteristic and the disturbance attenuation characteristic separately. From the practical point of view, it is desirable to specify the input-output characteristic and the disturbance attenuation characteristic separately. The purpose of this paper is to propose the parameterization of all stabilizing two-degree-of-freedom (TDOF) simple MPR controllers that can specify the input-output characteristic and the disturbance attenuation characteristic separately.

Improving the Tracking of Generalized Predictive Control Controllers

1996 ◽  
Vol 210 (3) ◽  
pp. 169-182 ◽  
Author(s):  
J A Rossiter ◽  
B G Grinnell
Keyword(s):  
Frequency Domain ◽  
Predictive Control ◽  
Time Domain ◽  
Degree Of Freedom ◽  
Generalized Predictive Control ◽  
Control Law ◽  
Set Point ◽  
The Poor ◽  
Fixed Order ◽  
Two Degree Of Freedom

One of the advantages of predictive control is its ability to take optimal account of information about future set point changes in the specification of the control law. However, the optimum GPC (generalized predictive control) prefilter that uses this information can lead to a deterioration rather than an improvement in the accuracy of tracking. Some simple modifications to GPC to overcome this problem are discussed. It will then be shown how some simple algorithms can be used to design an optimal prefilter that does not have any of the poor effects arising from the standard choice and hence always improves the performance. The basis of the technique is analogous to the two-degree-of-freedom designs common in the literature on H∞. However, here the emphasis is on fixed-order prefilters designed from a time domain, not a frequency domain, objective.

Performance-Adaptive Generalized Predictive Control-Based Proportional-Integral-Derivative Control System and Its Application

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Takao Sato ◽  
Toru Yamamoto ◽  
Nozomu Araki ◽  
Yasuo Konishi
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Pid Control ◽  
Design Method ◽  
Design Parameters ◽  
Generalized Predictive Control ◽  
Control Performance ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

In the present paper, we discuss a new design method for a proportional-integral-derivative (PID) control system using a model predictive approach. The PID compensator is designed based on generalized predictive control (GPC). The PID parameters are adaptively updated such that the control performance is improved because the design parameters of GPC are selected automatically in order to attain a user-specified control performance. In the proposed scheme, the estimated plant parameters are updated only when the prediction error increases. Therefore, the control system is not updated frequently. The control system is updated only when the control performance is sufficiently improved. The effectiveness of the proposed method is demonstrated numerically. Finally, the proposed method is applied to a weigh feeder, and experimental results are presented.

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