Two-degree-of-freedom linear quadratic Gaussian predictive control

1995 ◽  
Vol 142 (4) ◽  
pp. 295-306 ◽  
Author(s):  
M.J. Grimble
Keyword(s):  
Predictive Control ◽  
Degree Of Freedom ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
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.

Stability Robustness of LQ and LQG Active Suspensions

1994 ◽  
Vol 116 (1) ◽  
pp. 123-131 ◽  
Author(s):  
A. G. Ulsoy ◽  
D. Hrovat ◽  
T. Tseng
Keyword(s):  
Controller Design ◽  
Active Suspension ◽  
Point Of View ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Active Suspensions ◽  
Quarter Car Model ◽  
Stability Robustness ◽  
Trade Offs ◽  
Two Degree Of Freedom

A two-degree-of-freedom quarter-car model is used as the basis for linear quadratic (LQ) and linear quadratic Gaussian (LQG) controller design for an active suspension. The LQ controller results in the best rms performance trade-offs (as defined by the performance index) between ride, handling and packaging requirements. In practice, however, all suspension states are not directly measured, and a Kalman filter can be introduced for state estimation to yield an LQG controller. This paper (i) quantifies the rms performance losses for LQG control as compared to LQ control, and (ii) compares the LQ and LQG active suspension designs from the point of view of stability robustness. The robustness of the LQ active suspensions is not necessarily good, and depends strongly on the design of a backup passive suspension in parallel with the active one. The robustness properties of the LQG active suspension controller are also investigated for several distinct measurement sets.

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.

Application of Two Degree-of-Freedom Generalized Predictive Control to Temperature Control Experiment of an Aluminum Plate

2012 ◽  
Vol 132 (6) ◽  
pp. 879-885 ◽  
Author(s):  
Akira Yanou ◽  
Satoshi Okazaki ◽  
Junki Nishizaki ◽  
Shiro Masuda ◽  
Mamoru Minami ◽  
...  
Keyword(s):  
Predictive Control ◽  
Temperature Control ◽  
Control Experiment ◽  
Degree Of Freedom ◽  
Aluminum Plate ◽  
Generalized Predictive Control ◽  
Two Degree Of Freedom
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