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=Identiﬁcation for Model Predictive Control under Closed-loop Conditions= | =Identiﬁcation for Model Predictive Control under Closed-loop Conditions= | ||

− | '''Author''': | + | '''Author''': Eva Žáčeková |

[[Disertační práce 2019]] | [[Disertační práce 2019]] | ||

− | In recent years, modern control algorithms have gained popularity in many fields of | + | 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

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.