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(Založena nová stránka s textem „=Identification for Model Predictive Control under Closed-loop Conditions= '''Author''': Matej Pčolka Disertační práce 2019 In recent years, mo…“)
 
 
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=Identification for Model Predictive Control under Closed-loop Conditions=
 
=Identification for Model Predictive Control under Closed-loop Conditions=
  
'''Author''': Matej Pčolka
+
'''Author''': Eva Žáčeková
  
 
[[Disertační práce 2019]]
 
[[Disertační práce 2019]]
  
In recent years, modern control algorithms have gained popularity in many fields of industry. One of the methods that has become widely recognized is Model Predictive Controller (MPC).
+
In recent years, modern control algorithms have gained popularity in many fields of in-
 +
dustry. One of the methods that has become widely recognized is Model Predictive Con-
 +
troller (MPC). Such controller brings innumerable advantages such as possibility to define
 +
control requirements compactly as an objective function, ability to incorporate potential
 +
constraints directly into the cost function and many others—at the same time, it brings
 +
some  disadvantages  as  well.  Above  all,  its  main  drawback  is  the  fact  that  it  crucially
 +
needs a mathematical model for its proper functioning.  Its internal model not only has to
 +
describe the reality (the responses of the real controlled system) accurately but it should
 +
also be as simple as possible due to the computational complexity of the resulting task.
 +
However, a mathematical model that is not a sufficiently reliable replica of the controlled
 +
system can significantly degrade the performance yielded by the controller relying on it.
 +
Therefore,  extensive attention needs to be paid to the search for an appropriate system
 +
behavior predictor.

Latest revision as of 09:32, 5 November 2019

Identification for Model Predictive Control under Closed-loop Conditions[edit]

Author: Eva Žáčeková

Disertační práce 2019

In recent years, modern control algorithms have gained popularity in many fields of in- dustry. One of the methods that has become widely recognized is Model Predictive Con- troller (MPC). Such controller brings innumerable advantages such as possibility to define control requirements compactly as an objective function, ability to incorporate potential constraints directly into the cost function and many others—at the same time, it brings some disadvantages as well. Above all, its main drawback is the fact that it crucially needs a mathematical model for its proper functioning. Its internal model not only has to describe the reality (the responses of the real controlled system) accurately but it should also be as simple as possible due to the computational complexity of the resulting task. However, a mathematical model that is not a sufficiently reliable replica of the controlled system can significantly degrade the performance yielded by the controller relying on it. Therefore, extensive attention needs to be paid to the search for an appropriate system behavior predictor.